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University of Wollongong Thesis Collection University of Wollongong Thesis Collections
2013
A socio-rational approach to identifying andaddressing factors contributing to attrition in ICTdegrees in AustraliaMadeleine Rachel Helen RobertsUniversity of Wollongong
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Recommended CitationRoberts, Madeleine Rachel Helen, A socio-rational approach to identifying and addressing factors contributing to attrition in ICTdegrees in Australia, Doctor of Philosophy thesis, School of Information Systems and Technology, University of Wollongong, 2013.http://ro.uow.edu.au/theses/4181
School of Information Systems and Technology
A Socio-Rational Approach to Identifying and Addressing Factors Contributing to Attrition in ICT Degrees in Australia
MADELEINE RACHEL HELEN ROBERTS
"This thesis is presented in fulfilment of the requirements for the
award of the Degree of
DOCTOR OF PHILOSOPHY
from the
University of Wollongong"
June 2013
i
ABSTRACT
Student attrition is an issue of serious concern to universities around the world. Attrition
from ICT degrees is of particular concern, as is the lack of students commencing ICT
degrees, which have together reduced the number of potential future ICT professionals
(Cory, Parzinger & Reeves, 2006; Granger, Dick, Jacobson & Slyke, 2007; Zweben, 2008).
For the purposes of this study, attrition encompasses students enrolled in an ICT degree who
chose to transfer out of that degree to take up study in an unrelated area at their university or
who quit their university without completing any course of study.
Tinto’s (1975) Social Integration Model has been recognised as having “near paradigmatic
status” (Braxton, Milem & Sullivan, 2000) and is one of two models at the core of this study
alongside Bean’s (1980) belief that students do make rational decisions about why they
should quit. Although Tinto’s (1975) model has frequently been combined with the ideas of
other researchers (for example: Belch, Gebel & Maas, 2001; Braunstein, McGrath &
Pescatrice, 2001; Bray, Braxton & Sullivan, 1999; Ethington, 1990; Georg, 2009; Milem &
Berger, 1997; Munro, 1981; Pascarella, 1980; Woodard, Allory & De Luca, 2001), it was the
attempted combination of Bean’s (1980) and Tinto’s (1975, 1993) models by Cabrera, Nora
& Castaneda (1993) and Weng, Cheong & Cheong (2010) which gave rise to the socio-
rational approach taken in the current research.
Using this comprehensive approach, this study has identified the factors in the teaching and
learning environment in university ICT courses, and in students’ personal lives that
contribute to attrition, and has mapped these contributory factors to strategies found in the
literature, or suggested by ICT academics, or by members of the ICT industry. This has
demonstrated the utility of the socio-rational approach to attrition in ICT degrees in
Australia. Applying the socio-rational approach to attrition also allowed recommendations to
be made about the strategies that could be implemented to halt the decline in numbers of
students continuing with their study and thus prevent the negative outcomes for universities,
their students and the ICT industry.
University administrators and Deans and Heads of Schools of ICT could adopt the holistic
approach presented in this study to reduce the likelihood that the contributory factors that
have been identified would result in attrition of their students.
ii
ACKNOWLEDGEMENTS
Associate Professor Peter N Hyland
I am truly indebted to Peter for being my main Supervisor and I thank him for his
friendship, unwavering support, invaluable contributions along the way and ability to
intersperse the seriousness of research with liberal amounts of humour.
Dr Tony Koppi
Without Tony this thesis would not ever have been conceived. His willingness to
take on a fledgling Research Assistant for his two year research project made this
postgraduate degree possible.
Dr Holly Tootell
My thanks go to Holly for her willingness to be my number two Supervisor, for
providing answers to offbeat questions about obscure research issues and, most
importantly, for stepping into the breach when it was most needed.
Associate Professor Linda Dawson
I also want to thank Linda for her willingness to be my third Supervisor, if the need
had arisen, and for her input at a vital point in the process.
iii
TABLE OF CONTENTS
Abstract .................................................................................................................... i
Acknowledgements .................................................................................................. ii
List of Figures ......................................................................................................... vi
List of Tables ......................................................................................................... vii
1 Introduction .......................................................................................................... 8
1.1 Background to the Research ..................................................................... 9
1.1.1 Understanding the ICT Discipline ......................................................... 9
1.1.2 The Problem of Attrition ....................................................................... 9
1.2 Methodology ...........................................................................................11
1.3 Outline of the Thesis ...............................................................................14
1.4 Definitions...............................................................................................15
1.4.1 Attrition ...............................................................................................15
1.4.2 Higher Education Students...................................................................15
1.4.3 ICT Degree ..........................................................................................16
1.4.4 Tinto’s (1975) Social Integration Model ..............................................16
1.4.5 Bean’s (1980) Rational Decision Model ..............................................16
1.5 Delimitations of Scope and Key Assumptions .........................................17
1.6 Conclusion ..............................................................................................19
2 Literature Review ................................................................................................20
2.1 Introduction .............................................................................................20
2.2 Study of Attrition ....................................................................................20
2.2.1 The First Year Experience ...................................................................26
2.2.2 Individual Student Characteristics ........................................................27
2.2.3 ICT Degrees ........................................................................................33
2.3 The ICT Discipline ..................................................................................37
2.3.1 Problems Affecting the ICT Discipline ................................................39
2.3.2 Women Undertaking ICT Degrees .......................................................42
2.4 Attrition Reduction Strategies ..................................................................45
2.5 Taking the Socio-Rational Approach to Attrition .....................................54
2.6 Objectives of the Research.......................................................................59
2.7 Conclusion ..............................................................................................59
3 Methodology ........................................................................................................61
iv
3.1 Introduction .............................................................................................61
3.2 Research Paradigm ..................................................................................62
3.3 Context of the Research ...........................................................................65
3.4 Methods ..................................................................................................66
3.4.1 Candidate Socio-Rational Factors ........................................................66
3.4.2 The Survey Instrument.........................................................................67
3.4.3 Data Collection ....................................................................................71
3.4.4 The Questionnaire ...............................................................................73
3.4.5 The Interviews .....................................................................................79
3.4.6 Initial Analysis of Questionnaire and Interview Data ...........................79
3.4.7 Conducting the Capstone Interviews ....................................................83
3.4.8 Completion of the Six Research Objectives .........................................84
3.5 Ethical Considerations .............................................................................85
3.6 Conclusion ..............................................................................................85
4 Data Analysis .......................................................................................................87
4.1 Introduction .............................................................................................87
4.2 Student Data ............................................................................................88
4.2.1 Quantitative Survey Data .....................................................................88
4.2.2 Differences between Types of Students.............................................. 101
4.2.3 Open-ended Responses ...................................................................... 106
4.2.4 Interviewees and their Written Responses .......................................... 113
4.2.5 Factors Contributing to Attrition ........................................................ 117
4.3 ACDICT and ICT Industry Data ............................................................ 118
4.3.1 Members of ACDICT ........................................................................ 119
4.3.2 Members of the ICT Industry ............................................................. 124
4.4 Conclusion ............................................................................................ 127
5 Findings ............................................................................................................. 128
5.1 Introduction ........................................................................................... 128
5.1.1 Strategies Suggested by Members of ACDICT .................................. 132
5.1.2 Strategies Suggested by Members of the ICT Industry ....................... 135
5.2 Synthesis of Student, ACDICT and ICT Industry Data .......................... 140
5.3 ICT Student Attraction and Retention Initiative ..................................... 145
5.4 Outcomes of the Capstone Interviews .................................................... 148
v
5.5 Conclusion ............................................................................................ 152
6 Conclusion ......................................................................................................... 154
6.1 Introduction ........................................................................................... 154
6.2 Research Question and Objectives ......................................................... 154
6.3 Research Findings ................................................................................. 156
6.4 Significance ........................................................................................... 159
6.5 Limitations ............................................................................................ 161
6.6 Future Directions ................................................................................... 162
6.7 Conclusion ............................................................................................ 163
7 Bibliography ...................................................................................................... 165
Appendix A: Government (DEEWR) data on student attrition in ICT degrees ....... 181
Appendix B: Government (DEEWR) data on students commencing ICT degrees .. 182
Appendix C: Original survey tool from West et al. (1986) ..................................... 183
Appendix D: Tables showing the mapping of survey questions ............................. 184
Appendix E: SurveyMonkey student survey pages including welcome page ......... 189
Appendix F: Text for inclusion in correspondence to ex-ICT students ................... 190
Appendix G: SurveyMonkey spreadsheets ............................................................ 191
Appendix H: Sample of interviewee permission form ........................................... 192
Appendix I: ACDICT survey questions ................................................................. 193
Appendix J: ICT Industry survey questions ........................................................... 194
Appendix K: Human Research Ethics Committee permission letters ..................... 195
Appendix L: Tables of quantitative data ................................................................ 196
vi
LIST OF FIGURES
Figure 2.1 Commencements & attrition of students in Australian "IT" degrees 2001-
2008 (DEEWR, 2011) .....................................................................................36
Figure 2.2 Attrition % of commencing students in Australian "IT" degrees 2001-
2008 (DEEWR, 2011) .....................................................................................37
Figure 2.3 All commencing students in Australian "IT” degrees 2001-2008
(DEEWR, 2011) ..............................................................................................41
Figure 2.4 All commencing students in Australian "IT" degrees 2001-2008, by
gender (DEEWR, 2011) ..................................................................................42
Figure 2.5 Attrition of commencing female students in Australian "IT" degrees 2001-
2008 (DEEWR, 2011) .....................................................................................43
Figure 2.6 Attrition % of commencing students in Australian "IT" degrees 2001-
2008 by gender (DEEWR, 2011) .....................................................................45
Figure 3.1 Process steps in this research ..................................................................63
Figure 5.1 Women-friendly Club diagram depicting outreach and collaboration.... 147
vii
LIST OF TABLES
Table 2.1 Summary of attrition studies classified as Social (S) ................................57
Table 2.2 Summary of attrition studies classified as Rational (R) ............................58
Table 2.3 Summary of attrition studies classified as both Social (S) and Rational (R)
........................................................................................................................58
Table 5.1 Strategies to reduce attrition identified in the literature mapped to their
expected outcome(s) and indicating whether the strategy is Social (S), Rational
(R) or both (S&R) ......................................................................................... 129
Table 5.2 Strategies to reduce attrition identified in the literature and their focus or
expected outcomes mapped to the contributory factors identified in this research
...................................................................................................................... 131
Table 5.3 Strategies suggested by ACDICT to attract and retain students .............. 132
Table 5.4 Strategies suggested by ACDICT mapped to the focus or expected
outcome and classified as Social (S), Rational (R) or both (S&R) .................. 133
Table 5.5 Strategies suggested by ACDICT mapped to attrition contributory factors
...................................................................................................................... 134
Table 5.6 Strategies suggested by ICT industry to attract and retain students ......... 135
Table 5.7 Strategies suggested by ICT industry mapped to the focus or expected
outcome and classified as Social (S), Rational (R) or both (S&R) .................. 138
Table 5.8 Strategies suggested by ICT industry mapped to attrition contributory
factors ........................................................................................................... 139
Table 5.9 Strategies suggested by the literature (Lit), ACDICT members (ACD) and
members of the ICT industry (ACS) mapped to attrition contributory factors 144
8
1 INTRODUCTION
The use of Information and Communication Technologies (ICTs) now underpins the vast
majority of work and business life in the developed world and this trend is also increasing in
the developing world (Baggaley & Hoon, 2005; ITU, 2010). ICTs are now integral to our
personal and work communications, our finances, education, healthcare and entertainment
(Halford, Lotherington, Dyb & Obstfelder, 2010). As such, the role of ICT professionals is
vital in maintaining our current lifestyles. It is surprising, therefore, to find that there is a
shortage of ICT professionals in most developed countries (Gras-Velazquez, Joyce & Debry,
2009; ITU, 2012; Lewis, Lang & McKay, 2007). In Australia, for example, the Australian
Computer Society (ACS, 2008) identified a shortfall of 28,488 ICT staff for 2008, while
projecting a massive increase on this over the following decade, and its most recent
publication ‘Australian ICT Statistical Compendium’ (ACS, 2011) did not amend this
projection. In Europe it was reported that there were 28,000 IT positions unfilled in Germany
(Telecompaper, 2010) and the UK not-for-profit, employer-led organisation “e-Skills UK”
(2011), predicted that over half a million new ICT professionals would be needed in the next
five years. Globally, the International Telecommunications Union (ITU, 2012) predicts there
will be a shortfall close to 2 million in ten year’s time. Unfortunately, this abundance of
opportunities may already be wasted since there are not sufficient people – especially women
– trained to take their places.
There appear to be three main causes for this shortfall of ICT professionals:
• the retirement of ICT professionals who are part of the Baby Boom generation has
reduced the available pool (Crisp, Nora & Taggart, 2009);
• a lack of students commencing ICT training, particularly ICT degrees, has reduced the
number of potential future ICT professionals (Cory et al., 2006; Granger et al., 2007;
Lewis et al., 2007; Zweben, 2008);
• high levels of attrition from ICT training have compounded the problem (Bailey &
Borooah, 2007; Marks, 2007).
The first of these causes seems inevitable and the second is a complex, long-term problem
which requires a considerable shift in society’s perceptions of ICT as a profession (Craig,
9
Paradis & Turner, 2002; Koppi & Naghdy, 2009) from one inhabited by “geeky guys”
(Frieze, 2005) to one that garners respect. The third of these causes, however, could
conceivably be improved if we were able to identify the factors leading to attrition i.e. why
students quit ICT courses, particularly degree courses, and to suggest strategies to address
those factors. This could be achieved by surveying ex-students who have quit an ICT course
without completion and by mapping the identified factors to strategies identified in a
literature review and in surveys of members of ICT academia and individuals in the ICT
industry.
1.1 Background to the Research
1.1.1 Understanding the ICT Discipline
The term ICT is most often used to describe Information and Communication Technology
(Mulder, Lemmen & van Veen, 2002, p1; Webb, 2002, p237). Even if this more common
interpretation of the abbreviation is accepted there is still a great deal of debate about the
meaning of Information and Communication Technology and the term is frequently treated as
synonymous with Information Technology or IT. The general sense in the literature is that
ICT is the field associated with computers and telecommunications. A useful starting point is
the model of this field provided by the Association of Computing Machinery (ACM, 2005) in
their “Computing Curricula 2005” publication. This is a model which is widely understood to
represent the ICT discipline in Australia and contains six disciplines: EE (Electrical
Engineering), CE (Computer Engineering), CS (Computer Science), SE (Software
Engineering), IT (Information Technology) and IS (Information Systems) (ACM, 2005, p12).
1.1.2 The Problem of Attrition
Attrition is the process of a student quitting his or her course of study without completing it.
The term is used at both the institutional level e.g. when a student quits a university or
college, and at a course level e.g. when a student quits one course of study to take up another.
Tinto (1993), one of the leading researchers on student attrition, recognised that quitting
could be separated into three types of student “leaving” at the institutional level:
1. “permanent withdrawal from all forms of educational participation
2. immediate transfer to other institutions
3. only temporary withdrawal or stopout from studies” (Tinto, 1993, p141).
10
Since the current research is concerned with attrition from ICT courses at university, the first
two types are of central importance as quitting higher education completely, or leaving one
higher education institution for another, are both significant decisions impacting the
institution that loses the student. The third type of “leaving” is of less importance as it is
reasonable to assume that the student might return to complete the course at some later stage.
Eliminating that third type, the first two types can be modified to reflect the focus of this
study on ICT courses specifically. In this research, therefore, attrition will be when a student
quits an ICT course either because:
1. he or she has permanently left the institution altogether or
2. he or she has transferred to another non-ICT course.
The focus of the current study is on the university sector, so the institutions will be
universities, and the courses will typically be degrees.
Researchers agree that attrition from tertiary educational institutions is expensive and
wasteful (Bailey & Borooah, 2007, p1; Johnes & McNabb, 2004, p24; McMillan, 2005, p1;
Tinto, 1993, p139-140; Yorke, 1998, p190). So, not only is attrition from ICT degrees a
problem for the ICT industry but it is also costly for the government, for educational
institutions and for the individual. Universities depend upon student enrolments and
continuation for funding (Andrew, Salamonson, Weaver, Smith, O'Reilly & Taylor, 2007,
p866; Hinton, 2007, p14; Tinto, 1993, p139-140) while students incur significant costs from
which they gain little benefit if they quit before completion. Moreover, students who do not
complete a degree may see themselves as failures (Christie, Munro & Fisher, 2004, p618;
Tinto, 1993, p140) and may be emotionally damaged by the experience. To reduce these
costs it is necessary to determine what factors in a student’s personal life, or in their
experience of attending university, influence their decision to quit their study either by
leaving the university altogether or by transferring to a non-ICT degree.
Numerous studies have been undertaken to establish the reasons for attrition from tertiary
education in a number of countries, predominantly in Europe and North America. Many of
these have focussed on only one reason at a time, such as the lack of financial aid (Stater,
2009), the effect of boredom (Mann & Robinson, 2009) or the choices made by students with
dependent children (Marandet & Wainwright, 2010) while others have attempted to cover a
spectrum of reasons (Bailey & Borooah, 2007; Barker, McDowell & Kalahar, 2009;
11
Baumgart & Johnstone, 1977; Beekhoven, De Jong & Van Hout, 2002; Bennett, 2003;
Cabrera et al., 1993; Hovdhaugen, 2009; Nora, Barlow & Crisp, 2005; Price, Harte & Cole,
1992; West, Hore, Bennie, Browne & Kermand, 1986). Many of these investigations of
attrition do not state explicitly the hypothesis upon which their research is based as they are
building on, or adding to, the hypotheses of other researchers; a process which has been
ongoing since the beginning of the 20th century (Summerskill, 1965).
Two significant approaches to understanding the motivations of students, and identifying the
factors contributing to attrition, are those of Tinto (1975) and Bean (1980). Tinto (1975)
focussed on the social integration factors that militated against a student’s fitting into the
university environment and adjusting to the university experience. Bean’s (1980) view was
that students behaved rationally in weighing up the benefits of staying versus what could be
gained by leaving, just as employees may do in the workplace. These approaches have rarely
been combined, have not been used together in a comprehensive study and have only been
applied once to Information Systems students in Taiwan (Weng et al., 2010). It would seem
that combining the ideas of social integration and rational decision-making to take a socio-
rational approach to attrition might provide the means to acquire a greater understanding of
the process as it applies to students undertaking an ICT degree in Australia.
So, there is ample evidence that attrition from university and particularly from ICT degrees is
a worldwide problem that can lead to social, financial, and personal problems. The goal of
this research is twofold. The first is to assess whether the socio-rational approach will
provide a more comprehensive understanding of the factors contributing to attrition from ICT
degrees. The second is to demonstrate that using this approach will enable the mapping of
identified attrition-reducing strategies to these contributory factors.
1.2 Methodology
Given that the purpose of this research is to take a socio-rational approach to better
understand both the factors contributing to attrition, and to map those factors to appropriate
strategies, there is a need to gather data from a number of respondents from three quite
different cohorts. These three cohorts are students who have quit their ICT degrees, ICT
academics, and members of the ICT industry. The two most likely methods for doing this are
questionnaires or interviews. It is important to note that the literature expresses some
12
concerns about the ability of students to articulate their actual reasons for quitting, as some
students may present excuses for quitting rather than the real reason. Indeed, as a result of
either forgetfulness or of redaction some students may be unable to articulate their actual
reasons at some later time (Hoyt, 1978).
Notwithstanding these objections, it was decided to survey students who could be identified
as having quit an ICT degree. The literature provides a wealth of possible reasons for
attrition, however, the reasons suggested by the literature were often reasons for quitting
university altogether, which might not include reasons for quitting an ICT degree,
specifically. Where reasons did relate to previous overseas studies of attrition from an ICT
degree, it was possible that additional reasons existed in the Australian context.
Consequently, a series of interviews were conducted to ensure that a comprehensive range of
reasons for attrition from an Australian ICT degree were explored and understood.
The approach taken to gather data for this research utilised mixed methods using a sequential
transformative strategy (Creswell & Plano Clark, 2007, p212) in four phases. The first phase
involved the gathering of quantitative data such as Australian government statistics on
students and also from Australian universities. The second phase was the gathering of
quantitative data in two stages:
1. The first stage was the creation of a comprehensive survey instrument, based on the
socio-rational approach, to gather data from ex-ICT students.
2. The second stage entailed surveying members of the Australian Council of Deans of
ICT (ACDICT) and members of the ICT industry.
The third phase was the acquisition of qualitative data from those survey respondents willing
to be interviewed and the fourth phase was gathering of qualitative data from leading ICT
academics who participated in capstone interviews. Although qualitative data is not always
viewed as highly as quantitative data, the explanatory powers of qualitative data should not
be dismissed (Creswell & Plano Clark, 2007; Hesse Biber, 2010) and are viewed as equally
valuable in understanding the factors contributing to attrition in this study.
To establish that there is, indeed, a persistent level of attrition in ICT degrees in Australia
which mirrors that of other western industrialised countries, it was necessary to establish both
the level of commencements as well as the amount of attrition occurring over a period of time
13
which would allow trends to be revealed. To that end, ICT degree attrition and
commencement statistics for the period 2001 to 2008 (see Appendices A & B) were ordered
from the Department of Education, Employment and Workplace Relations (DEEWR) in
2010.
Since there have been a number of previous surveys conducted on students who have quit
their courses, and the concepts are well understood, it is reasonable to conduct a survey of
Australian students to identify their reasons for quitting their study of ICT. Rather than start
from scratch, or use a questionnaire that had been created by researchers outside Australia,
this study identified a questionnaire which had previously been utilized successfully to gather
data from ex-students in the Australian higher education context (Price et al., 1992; West et
al., 1986), the majority of which was of a quantitative nature. This instrument, which was
designed by West et al. (1986) and reused by Price et al. (1992), informed the development of
the survey used in this study. The modified survey instrument was used in the ALTC project
as the primary data collection tool in the current context.
Rather than gather data only from ex-ICT students, it was reasoned that those involved in
teaching and administering ICT courses at university should also be consulted, as they would
have counselled students experiencing difficulties with their classes and acquired an
understanding of the variety of issues that plague students during their study. These ICT
academics would also be familiar with the strategies used to address these issues and which
of those interventions had been successful. While this information, alone, would be
invaluable, it was also recognised that members of the ICT industry, many of whom are ICT
graduates, could also provide insights into their experience of studying at university.
Although quantitative data has been used extensively to gain insights into attrition in the past,
it does not provide a complete understanding of the motivations and reasoning of students
who decide to withdraw from study. To compliment the quantitative data that could be
gathered from the ex-ICT students it was reasoned that the only people who could explain
why they had quit an ICT degree were the students who had, in fact, done so. Conducting
interviews with those students would assist in identifying the factors leading to attrition as it
would allow a clearer understanding of the potential multitude of experiences and events
which may have contributed to their quitting.
14
Although the combination of data from three sources would produce a far more
comprehensive investigation of attrition than those commonly found in the literature, this
study advanced the research further by taking a socio-rational approach to understanding the
factors contributing to attrition. This approach was also utilised during the process of
mapping the factors, identified by the ex-ICT students as those leading to attrition, to the
strategies identified in the literature and in the surveys of ACDICT and ICT industry
members. As this socio-rational approach to understanding the factors contributing to attrition
is unique, a further step was taken to validate its effectiveness by interviewing experts from
academia. It is expected that the results of this study will assist both industry and academia to
understand more fully the issues faced by students undertaking ICT degrees in Australia and
also indicate what might be done to address the factors contributing to attrition.
1.3 Outline of the Thesis
Following this introductory chapter will be a review of the literature on attrition from
university study, generally, and ICT study, specifically, in order to identify the various
reasons students give to explain why they have quit their study altogether or have transferred
away from ICT to an unrelated area of study. The delineation of these reasons as either social
(S) or rational (R) will establish the foundation for the proposed socio-rational approach.
Chapter 3 will discuss in detail the methodology used to identify the factors leading to
attrition from ICT degrees and to map those to existing strategies which might address those
factors. Chapter 4 will present both qualitative and quantitative data collected from ex-ICT
students, members of academia and people employed by the ICT industry, together with its
statistical and interpretive analysis. Chapter 5 will examine the strategies currently being used
or those recommended by members of academia and industry and synthesise all the data
gathered.
The sixth and final chapter will present the conclusions drawn from the data and its analysis
which identified factors in students’ experiences of their university’s environment, their ICT
courses and their personal life that may have contributed to attrition, revealed strategies used
to reduce student attrition and, finally, mapped those strategies to the contributory factors
identified by students in order to validate the socio-rational approach. The implications of
15
these findings will be presented, the limitations of the research will be acknowledged and
recommendations for action will be made.
1.4 Definitions
To ensure the meanings of terms used throughout this research are clearly defined, the
following sections will cover: Attrition; Higher Education Students; ICT Degree; Tinto’s
(1975) Social Integration Model; and Bean’s (1980) Rational Decision Model.
1.4.1 Attrition
For the purposes of this study, attrition encompasses students enrolled in an ICT degree who
chose to transfer out of that degree and take up study in an unrelated area at their university
or who quit their university, either by expulsion or choice, without completing any course of
study.
A number of terms have been used to describe the manner in which a student indicates their
inability or unwillingness to pursue their higher education such as discontinuance,
withdrawal, non-completion, mortality and dropout. For the purposes of this study the terms
“quit” or “withdraw” will indicate attrition of a student from ICT study while “transfer” will
describe students who change to an unrelated course in the same university.
1.4.2 Higher Education Students
Entry to university is predicated upon achieving a predetermined examination score which, in
Australia, in known as the Equivalent National Tertiary Education Rank (ENTER). This
score (or its equivalent) is routinely included as a characteristic of students. Once enrolled,
this homogenous cohort can then be divided on the basis of a number of other characteristics
such as:
• age; • gender; • prior educational achievement; • ethnic background; • socio-economic status; • marital and parental status; and • study load (full-time or part-time).
16
In this research the focus will mainly be on domestic students who are: traditional or mature
age; female or male; studying ICT at undergraduate level; of any ethnic or socio-economic
background; married or single with or without a dependent child or children; and enrolled
full-time or part-time.
1.4.3 ICT Degree
Establishing an agreed definition of an ICT degree is one which has proved difficult as Craig
(2010, p14) acknowledges that it “lacks clarity.” For the purposes of this research an ICT
degree will be one that comprises any or all of the following: computer science, information
technology, information systems, software engineering, computer systems engineering,
telecommunications engineering or electrical engineering.
1.4.4 Tinto’s (1975) Social Integration Model
Tinto has been identified as a leader in the field of student attrition with his seminal work
having been cited hundreds of times (Braxton, Hirschy & McClendon, 2004). Tinto (1975)
focussed on the social integration factors that militated against a student’s fitting into the
university environment and adjusting to the university experience. In doing so he shifted the
explanatory focus from student characteristics upon entering college (i.e. educational
aspirations, academic ability, and family background) to their success in entering and
integrating into the higher education academic and social systems as well as acknowledging
the potential effects of the student’s personal life in the outside world. Tinto (1975) also
introduced the concept of cost-benefit analysis as a way to understand the motivations of
students when they considered whether to stay or quit. Tinto’s (1975) recognition of the
social aspect of higher education, and its effects on those who were entering for the first time,
put his model in contention with the majority of the ideas that had been proposed before it.
For the purposes of this research, Tinto’s (1975) Student Integration Model will be referred to
as “Tinto’s (1975) Social Integration Model”.
1.4.5 Bean’s (1980) Rational Decision Model
Bean was critical of Tinto’s (1975) Social Integration Model as his view was that higher
education students behaved rationally in weighing up the benefits of staying versus what
could be gained by leaving, just as employees may do in the workplace. In the case of
employees, pay can be a major determining factor in the decision to stay or move on and
17
Bean (1980, p157) suggested three possible “surrogate measures”: grades received; personal
development; and potential to gain employment that students might use to make their
decision to continue with study or quit. Thus college GPA was combined with the student’s
assessment of: the practical value of their education; their own intellectual development; and
the quality of the educational institution they were attending. These four variables, together
with 19 others, were thought to affect the student’s satisfaction with and commitment to their
educational institution.
For the purposes of this research Bean’s (1980) Student Attrition Model will be referred to as
“Bean’s (1980) Rational Decision Model”.
1.5 Delimitations of Scope and Key Assumptions
For the purposes of this research, attrition will be demonstrated by statistics gathered from
the Australian government and Australian universities. The government statistics will reveal
the trend in attrition over the period 2001 to 2008 while the statistics gathered from
questionnaires completed by ex-ICT students at various Australian universities will cover a
shorter time period (2005 – 2010). These figures will be presented to demonstrate that
students do quit their study of ICT at university and provide a snapshot of the rate at which
attrition has occurred.
This study investigates attrition of a particular group within the university cohorts of 2005 to
2010 in four universities in Australia. Since this is a small proportion of the 37 public
universities Australia-wide (Bradley, Noonan, Nugent & Scales, 2008) the findings of this
study will be an indication of the ways in which students are affected by their experiences of
attending university, while it is recognised that there are likely to be more factors which may
contribute to attrition in specific places that this study will not address.
The four universities involved in this study were originally selected for a research project
funded by the Australian Learning and Teaching Council (ALTC) with the signifier: PP9-
1274 entitled “Addressing ICT Curriculum Recommendations from Surveys of Academics,
Workplace Graduates and Employers” (Ogunbona, Naghdy, Koppi, Armarego, Bailes,
Hyland, McGill, Naghdy, Pilgrim & Roberts, 2013). The origins of this PhD research are
18
founded in this project. The author of this thesis was employed as a Research Assistant on the
project and was given the opportunity to continue the project work as a HDR student.
The ALTC project had five main considerations: Perceptions, Attrition, Gender, Teaching-
Research-Industry-Learning nexus and Work Integrated Learning. The PhD author was
predominantly responsible for all research related to attrition, in the first instance, and then
expanded to gender as the ALTC project continued. The PhD candidate, in her role as
Research Assistant, was responsible for researching and developing the survey instrument
that was used in the ALTC project. ALTC project leader Dr Tony Koppi was instrumental in
ensuring that there was a substantial division of originality between the outcomes of the
ALTC project and this thesis. The defining division of work between the ALTC project and
this PhD is found in the original methodological socio-rational approach that allowed this
thesis work to be grounded in theory and provides a framework for understanding.
The senior academics selected by Dr Tony Koppi for the ALTC project were chosen on the
basis that they had already demonstrated a keen interest, and a track record of research, in the
areas to be investigated. Those academics were, at that time, members of staff at the
University of Queensland, the University of Wollongong, Swinburne University of
Technology and Murdoch University (Koppi, 2012). As the universities were selected
because they were employing these academics, it is important to establish that they are
reasonably representative of universities around Australia. To that end, the following facts are
presented. Three of these four universities are located on the east coast of Australia with the
fourth (Murdoch) located on the west coast. Three of the universities are located near the
capital cities of their respective states, while the fourth, Wollongong, is situated 80 kilometres
from its capital city.
Although it is recognised that this is a small sample of universities and their locations limit
the scope of this study, they do vary in cohort size with Murdoch University (2012) being the
smallest (18,000) and the University of Queensland (2012) being the largest (45,000), they do
offer a range of ICT degrees (for one example of this, see section 2.2.3) and each belongs in a
different DEEWR university classification. Those classifications are as follows: University of
Queensland – Group of Eight; University of Wollongong – Metropolitan; Murdoch
University – Innovative Research Universities; and Swinburne University of Technology –
19
Technology (Godfrey & King, 2011). So it appears that the four participating universities are
sufficiently representative to allow the findings to be broadly generalised to the Australian
student cohort.
There are also limitations to the data collected from ICT academics and members of the ICT
industry. The ICT academics who were asked to participate are members of the Australian
Council of Deans of ICT (ACDICT). While a reasonable number of surveys were returned by
the ACDICT members, the respondents remain anonymous, so it is not possible to claim that
these surveys represent the majority of the 37 universities asked to participate nor can it be
said that the member of ACDICT actually completed the survey as it is reasonable to assume
that the Dean of ICT may have passed on the survey to their Head of Teaching, for example.
The survey of members of the ICT industry was conducted in two phases: the Advisory
Board members of the four partner universities, who are current or former members of the
ICT industry, received a survey in the mail; and the members of the Australian Computer
Society (ACS), a peak body for ICT practitioners, were invited to participate in an online
survey which was publicised through their branch newsletters and promoted on the ACS
website. As participation was voluntary, only those who completed a survey via the process
of self-selection have provided information. Although it may be reasonable to state that this
information is generally representative of the views of people working in the ICT industry, it
cannot be said to encompass all the views held on the questions asked.
1.6 Conclusion
This chapter has presented the background to the research and the methodology to be used, as
well as outlining the structure of the thesis. Five specific terms used throughout the thesis
were defined and the delimitations of scope and key assumptions of the research were
acknowledged. The following chapter is a review of the literature on attrition of students in
higher education, generally, and of ICT students specifically. There is also consideration
given to the attrition reduction strategies proposed and or implemented by researchers before
the unique socio-rational approach to understanding attrition is presented, followed by the
research question and the six objective of this research.
20
2 LITERATURE REVIEW
2.1 Introduction
The previous chapter presented the context of the current research and stated the twofold goal
of the research to be the use of a comprehensive socio-rational approach to identify factors
contributing to attrition from ICT degrees, and to map those factors to strategies found in the
literature, utilised by ICT academics and suggested by members of the ICT industry. By this
means it may be possible to identify ways in which the recognised decline in numbers of
students commencing and continuing with their ICT study may be slowed.
To achieve this, it is necessary to understand the history of research into the causes of
attrition in higher education and how attrition in ICT degrees, specifically, has been
investigated. These will be explored at the beginning of this chapter and will be followed by
an examination of a selection of attrition reduction strategies and the application of the socio-
rational approach to the objectives of the research.
2.2 Study of Attrition
Some of the earliest comprehensive studies of student attrition were conducted in the U.S.
(Blakely, 1928; McNeely, 1938) and, although there were some attempts to understand
attrition in Australia in the 40s (Sanders (1948) cited in Krause, Hartley, James & McInnis,
2005) and 50s (Hohne (1951) cited in Baumgart & Johnstone, 1977) by Hammond (1957),
Priestley (1958), Gray (1958), Flecker (1958) and Theobald (1959) (cited in Krause et al.,
2005, p94), the most influential thinkers on attrition do appear to have been predominantly
American, up until the 90s. The following is a brief recounting of the research that informed
the models of Tinto (1975) and Bean (1980) which are used in this study.
Astin (1964) began investigating student attrition in colleges and universities at the beginning
of the 60s, bringing together the work of Iffert (1957), Gough (1957), Stern, Stein & Bloom
(1956), and others. Individual characteristics of students entering college were believed to
play a significant role in attrition at that time. The characteristics most frequently identified
as having the potential to indicate which students would succeed and which would fail were:
high school grade point average (GPA); various psychological typologies; socio-economic
status; and number of applications for scholarships. Although Astin and Holland (1961) used
21
these measures, they also recognised that the “college environment” contributed to the
problem. One of the measures Astin (1964, p225) employed was the “Masculinity of an
institution” which he found increased the chances that women would quit. Astin (1964)
theorised that the greater the number of men on a campus, the higher the probability for
women students to date, resulting in an early marriage which, at that time, was expected to
result in women students quitting their studies. An alternative theory was that a woman
would find the “intellectual climate of the male institution ... incompatible with her own
interests and aspirations” (Astin, 1964, p225) without giving further explanation of his
thoughts on the “climate” or why it would be incompatible for women.
Spady (1970), who considered Astin’s (1964) work along with many others in his
comprehensive review of the literature, revealed that research into student attrition in
America had been substantial throughout the 50s and 60s. While emphasising his desire for a
theoretical synthesis of findings that was methodologically and conceptually fruitful, Spady
(1970, p64) did acknowledge that “no one theoretical model can hope to account for most (let
alone all) of the variance in dropout rates either within or across institutions.” In his doctoral
dissertation (Spady, 1967), and subsequently, Spady (1970) identified several factors that
occur, again, in the works of other notable American thinkers who followed immediately on
his heels. It was Spady (1970, p77), for example, who suggested that grades were “tangible
resources in the quasi-occupational role-playing of the career-oriented student”; a concept
that was later used by Bean (1983). Spady (1969) also used the concepts of social integration
and the rites of passage which were taken up, at different times, by Tinto (1975, 1993). It was
Spady’s (1971) belief – based on Durkheim’s (1951) assertion that suicide was the result of
an individual’s failure to integrate into society – that “the successful assimilation of entering
college students into the full life of their institution” should be seen “as problematic rather
than as a given” (Spady, 1971, p39) which also influenced Tinto (1975) whose work has been
acknowledged as having “near paradigmatic status” (Braxton et al., 2000, p569).
The influence of Tinto (1975) is also evident in the early work of Pascarella and Terenzini
(Pascarella, 1980; Pascarella & Terenzini, 1977, 1983; Terenzini & Wright, 1987) in which
they used his 1975 model as the basis for their research into student attrition, which both
Pascarella (Pascarella, Salisbury & Blaich, 2011) and Terenzini (Reason, Terenzini &
Domingo, 2005, 2007) continue to investigate. Both acknowledge the major contribution
22
made by Tinto (1975) in shifting the explanatory focus from student characteristics upon
entering college (i.e. educational aspirations, academic ability, family background) to their
success in entering and integrating into the higher education academic and social systems.
Later, Bean (1985) used the contributions made by Pascarella and Chapman (1983) on
Tinto’s (1975) model and, significantly, their conclusion that “[p]erhaps a major portion of
persistence/withdrawal behavior (sic) is so idiosyncratic, in terms of external circumstances
and personal propensities, that it is difficult to capture in any rational explanatory model”
(Pascarella & Chapman, 1983, p99). Pascarella and Chapman (1983, p100) do, however,
acknowledge that they viewed Tinto’s (1975) model as being a potentially useful framework
for understanding the process of student decision-making about persisting or withdrawing
from postsecondary education.
Tinto’s (1975) Social Integration Model, as it relates to new students entering the university
system, has been utilised by many researchers, including Braxton et al. (2004), who
acknowledged that there were more than 775 citations of Tinto’s work by the year 2004.
Tinto’s (1975) recognition of the social aspect of higher education, and its effects on those
who were entering for the first time, put his ideas in contention with the majority of those that
had been proposed before it. Researchers investigating student withdrawal in the decades
before Tinto’s model, had viewed withdrawal as primarily the fault of the student (Tinto,
2006, pp3-4) and had not recognised that the environment students entered may have seemed
hostile to some.
Tinto’s (1975) original model differentiated between the student’s ability to fit into the social
and the academic lives of the university, as well as acknowledging the potential effects of the
student’s personal life in the outside world. Tinto (1975) also introduced the concept of cost-
benefit analysis as a way to understand the motivations of students when they considered
whether to stay or quit. In presenting his belief that lack of integration into the college’s
social system would lead to low commitment to it, and result in an increased probability that
they would quit, Tinto (1975) acknowledged Spady’s (1970) first application of Durkheim’s
theory of suicide on student attrition.
Notwithstanding the high regard in which Tinto’s (1975) model is held, there are aspects of it
that do not readily transfer across time (it was a given that the majority of students studied
23
full-time, and lived on campus in either halls of residence or fraternities/sororities) or to all
parts of the western industrialised world (the choice between public and highly selective
private universities, the availability of two or four-year degrees, and the fraternity/sorority
and scholarship systems are not elements of the higher education schema in all countries).
Like Tinto’s (1975) model, Bean’s (1980) Rational Decision Model of student attrition also
contained the variable “integration” but only as one of several factors which he combined
into three categories: background variables; organisational determinants; and intervening
variables. Bean’s model was based on his belief that the reasons people change jobs could be
modified and used to explain why students withdraw from college. This has also had a
notable effect on subsequent research into student attrition as it introduced the idea that
students, like employees, act rationally by weighing up the benefits of remaining in their
current situation. In the case of employees, pay can be a major determining factor in the
decision to stay or move on and Bean (1980, p157) suggested three possible “surrogate
measures”: grades received; personal development; and potential to gain employment that
students might use to make their decision to continue with study or quit.
In Bean’s (1980) seminal paper he was critical of both Tinto (1975) and Spady’s (1970) use
of Durkheim’s (1951) theory of suicide. Bean (1980) asserted that there was insufficient
evidence for their premise that attrition and suicide had similarities. He also criticised the
models put forward as graphical depictions of the process of attrition by both Spady (1970)
and Tinto (1975) because “strict attention was not paid either to the recursiveness (directional
causality) ... or ... discreteness of the variables” (Bean, 1980, p156). Bean’s response was to
look elsewhere for another, more satisfactory, model to explain attrition causality. The model
he found was one used by Price (1977) on employee turnover in organisations which Bean
(1980) viewed as analogous to student attrition. As had been suggested by Tinto (1975), Bean
(1980) believed that students contemplated the costs versus benefits of continuing their study
and that Spady’s (1970) identification of grades being used as evidence of success in the
“quasi-occupational role-playing of the career-oriented student” was a useful substitute, in
part, for the pay variable. Thus Bean (1980) combined college GPA with the student’s own
assessment of: the practical value of their education; their intellectual development; and the
quality of the educational institution they were attending. These four variables, together with
19 others, were thought to affect the student’s satisfaction with and commitment to their
24
educational institution. Overall, Bean’s (1980) research found that institutional commitment
was the most important variable in explaining attrition for both men and women.
Since Tinto (1975) was only contemplating the problem of attrition as it manifest in the
United States, some of the accepted features of the college experience, as outlined earlier,
remained when he conceded, some years later (Tinto, 1982), that his original model had not
accounted for factors that had been, and continued to be, influential in the process of quitting.
Tinto (1982, p689) specifically named some of the failings in his original model which did
not adequately: consider the role of finances; distinguish between those behaviours that lead
to institutional transfer and those that result in permanent withdrawal from higher education;
or acknowledge the important differences in educational careers for students of different
genders, races, and social status backgrounds.
Despite its failings, many researchers, including Pascarella and Terenzini (1983) continued to
work on validating the basic premises upon which Tinto’s (1975) model was based. Their
work found that there were “statistically significantly compensatory interactions between
social and academic integration and between institutional and goal commitment” (Pascarella
& Terenzini, 1983, p225). Pascarella and Terenzini (1983) also believed that social and
academic integration were not independent of each other and that one might compensate for
lower levels of the other.
Up to this point, the student’s active participation in assimilating into their new environment
does not appear to have been considered. In 1985, Bean presented his “dropout syndrome”
theory and cited the influence of “exchange theory” (Homans (1961) cited in Bean, 1985) and
“expectancy theory” (Lawler (1973) cited in Bean, 1985). Bean (1985, p36) characterised his
theory of dropout syndrome as a student’s “conscious, openly discussed intention to leave an
institution coupled with actual attrition.” He also identified a form of quitting that was novel
in that it accounted for students who did not intend to leave but must do so because of ill
health or a crisis in their family (Bean, 1985).
Clearly student attrition in higher education is a complex issue and researchers in the past
have acknowledged this (McInnis, Hartley, Polesel & Teese, 2000a; Pascarella & Chapman,
25
1983; Spady, 1970). The difficulties associated with determining the actual reasons or causes
of attrition are summed up by the following four points:
1. “real reasons may evade the researcher;
2. the former student may be unwilling to identify the real reason;
3. multiple reasons may exist and it may be impossible to disentangle individual
contributions;
4. reasons may be confused with actions (e.g. “got a job” may be due to lack of money,
lack of interest in the course, as so on).” (Hoyt cited in Price et al., 1992, p7).
Not only are there problems associated with identifying the real reasons for attrition, there are
also levels of complexity in identifying what is meant by the term attrition, determining when
attrition has occurred, and understanding the ramifications of attrition for the university and
the student.
Attrition is a concern at both the level of the institution i.e. the university and at the course
level i.e. the degree. Seidman’s (2005, p92) description of attrition as simply a “diminution in
numbers of students resulting from lower student retention”, and Price et al.’s (1992, p7)
statement that it is a “conscious decision to leave a course unfinished when the individual is
eligible to continue” could apply at either level. Hinton’s (2007) comprehensive definition of
attrition, however, is more suitable at the university level. Hinton (2007) identifies nine forms
of attrition encompassing all stages of the offer and enrolment process including the
following four outcomes after enrolment:
1. cancelled their program of study;
2. withdrew from courses;
3. failed their course; or
4. transferred to another institution (Hinton, 2007, p16).
A much simpler definition of attrition is used by the Australian government Department of
Education, Science and Training (DEST) which states that it is “the proportion of students in
a particular year who neither graduate nor continue studying in an award course at the same
institution in the following year” (DEST, 2004, p2). While Tinto (1993) identified three types
of student “leaving” as:
1. “permanent withdrawal from all forms of educational participation
26
2. immediate transfer to other institutions
3. only temporary withdrawal or stopout from studies” (Tinto, 1993, p141).
The current study will focus on the first two of these as it can be assumed that students who
withdraw temporarily from their studies may return at some stage in the future.
2.2.1 The First Year Experience
Comprehensive studies of the first year experience of students at university have only been
undertaken in Australia since the 90s. The first of these national Federal Government-funded
studies in Australia was conducted in 1994 (Krause et al., 2005) with the most recent being
completed by James, Krause & Jennings (2010) which discusses the last cohort of first year
students to enrol prior to the publication of the Bradley Review (James et al., 2010, p1). In
their report, James et al. (2010) not only make comparisons with findings from the previous
studies but also highlight several areas on which universities should focus, including student
income support to reduce the number of hours worked to provide necessities, monitoring the
amount of time students spend on study, strengthening student-teacher interactions,
monitoring students who are more likely to quit or perform poorly and being more explicit in
enunciating the expectations the university has of entering students (James et al., 2010, pp72-
74).
One of the fundamental expectations is that students arriving at university will have a certain
level of prior educational attainment. This demonstration of academic ability is expected to
be a reliable indicator of a student’s capacity to undertake a university degree (McKenzie &
Schweitzer, 2001, p22). There are, however, other factors at play once students begin their
studies and it has become clear that the first year is the make or break period (Nelson, Kift &
Clarke, 2008; Norton, 2010) and some conclude at an early stage that the issues they
encounter prove too difficult to overcome.
Researchers who have investigated the first year experience for students of all ages have
considered the influence of students’ personal characteristics and circumstances (Bailey &
Borooah, 2007), the quality of instruction on student persistence (Pascarella et al., 2011), the
effects of university interventions (Tumen, Shulruf & Hattie, 2008), whether the timing of
27
quitting varies between age groups (Andrew et al., 2007) and what can be learned from
reviewing national studies conducted in Australia (Krause et al., 2005).
2.2.2 Individual Student Characteristics
There have been thousands of studies of attrition over the decades (see, for example, the
overviews by Pantages & Creedon, 1978; Pascarella & Terenzini, 2005; Spady, 1970) and
those studies can be broadly categorised as those trying to understand the psychological
motivations of students and those using numerical data gathered from students’ demographic
characteristics. As this study did not investigate students’ psychological characteristics, the
following is a summary of research using students’ individual demographic characteristics:
age; gender; prior educational attainment; ethnic background and socio-economic status
which are the most widely studied, followed by subsections on: marital and parental status;
and study load.
2.2.2.1 Age
It is frequently assumed that undergraduate students are those who have continued their
education, without a break, and enter university at the ages of 17, 18 or 19 years old. These
students are commonly referred to as “traditional”, while those who are 20 or older are
classified as “mature age”. Some authors (Hagedorn, 2005; Kramer, 2007; Nora et al., 2005;
Price et al., 1992; Tinto, 1993) have chosen to examine and discuss students as a homogenous
entity while others have recognised the differences in experiences for those in the traditional
(Christie et al., 2004; Harrison, 2006; Hillman, 2005; Tinto, 1988; Trotter & Roberts, 2006)
and mature age categories (Clark & Ramsay, 1990; Marandet & Wainwright, 2010; McInnis,
James & McNaught, 1995; Price et al., 1992; Trotter & Cove, 2005). Others consider
students of all ages (Andrew et al., 2007; Bailey & Borooah, 2007; Buglear, 2009; Krause et
al., 2005; Pascarella et al., 2011; Tumen et al., 2008).
Studies of traditional age students consider factors such as the process of admission and
whether the standards for determining entry were appropriate (Lewis, 1928; Margolis &
Fisher, 2002), the effects of the orientation process (Lewis, 1928; Trotter & Roberts, 2006),
the contribution made by poor study and time management skills (Trotter & Roberts, 2006),
whether negative experiences and dissatisfaction with their course (Christie et al., 2004;
Harrison, 2005) or their ability to achieve a balance between work and study (Hillman, 2005)
and fit in with others in a new environment (Tinto, 1988) contributed to their decision to quit.
28
An Australian study investigating age differences of students (Krause et al., 2005) found that
there are specific differences between mature age and traditional students. Mature age
students had clear goals, were strategic in their approach to study, believed they were
receiving the help they required, enjoyed the challenges associated with the learning process
but tended to keep to themselves in class and not participate in extracurricular activities
either. The traditional age students, by comparison, were more likely to skip classes, want to
change courses and be unmotivated to study. However, mature age students were more often
enrolled part-time and more likely to have work and family commitments interfering with
their study (Krause et al., 2005, p73) than those in the younger age bracket. The findings for
mature age students in the Krause et al. (2005) study are supported by more recent research
(Halpern, 2007) in the UK and were also identified in Australia two decades ago (Clark &
Ramsay, 1990; Price et al., 1992).
The expectation that most students entering university are straight out of high school is no
longer valid (James et al., 2010, p14), however, so the concerns of adulthood, such as the
rearing of dependent children or caring for an aged parent, cannot be ignored. Some studies
find that being a mature age student is an advantage (Halpern, 2007; Krause et al., 2005;
McInnis, James & Hartley, 2000b) while others suggest the opposite (Cartney & Rouse,
2006; Christie et al., 2004; Clark & Ramsay, 1990; Price et al., 1992).
2.2.2.2 Gender
Gender is a well-defined concept when it is presented as a simple dichotomy: male or female.
There are studies of attrition that do not differentiate between the genders but others
recognise that there are benefits in understanding why and how their experiences differ and
what effects that has on the students’ decision to stay or quit (Barrow, Reilly & Woodfield,
2009; Charles & Bradley, 2006; Manis, Sloat, Thomas & Davis, 1989; Seymour & Hewitt,
1997).
2.2.2.3 Prior Educational Achievement
Determining a student’s ability to succeed at university has been and still is based almost
solely on their success in achieving results in high school exams that allow them to compete
for a place at university (Bradley et al., 2008; James, Bexley, Anderson, Devlin, Garnett,
Marginson & Maxwell, 2008). As entry to university is predicated upon achieving a
predetermined examination score, which may be referred to as Grade Point Average (GPA),
29
Scholastic Aptitude or Assessment Test (SAT) or, in Australia, as the Equivalent National
Tertiary Education Rank (ENTER), there is an assumption that students arriving at university
will have a certain level of prior educational attainment. This has also been used as a measure
to determine the likelihood of their continued success once enrolled in higher education
(Braxton et al., 2000; Pascarella & Terenzini, 1983; Tinto, 1975). Although a much larger
proportion of the university population is now composed of mature students with life and
work experience, universities still impose surrogate measures which might require potential
students to undertake a certain amount of pre-university education to demonstrate that they
have the required level of intellect and ability. Their prior learning experiences might also be
of benefit to them, if it is recognised as sufficient to permit them to gain credit for certain
introductory courses. These allowances in current university administrative processes
demonstrate that systems and prerequisites can be modified to take new circumstances into
consideration but none of these truly shift the underlying expectations that inform them.
Consequently, a measure of intellect and ability remains a requirement of entry into
university and is at the evaluative core of determining a person’s capacity to learn and,
therefore, persist (Braxton et al., 2000; Davies, Bentley & Holland, 2004; Nora et al., 2005;
Pascarella & Terenzini, 1983; Tinto, 1975, 1993). This score is routinely included as a
characteristic of students and frequently added to a range of other demographic data collected
for research (Bean & Metzner, 1985; DuBrock, 1999; Ishitani, 2006; Marks, 2007; Tinto,
1975; West et al., 1986).
2.2.2.4 Ethnic Background
Although ethnic background has begun to be recognised as a potential factor in attrition of
students, Bean (1980) used it to exclude students from his study while others (Barker et al.,
2009; Bean & Metzner, 1985; Heemskerk, Brink, Volman & ten Dan, 2005; Lewis, Smith,
Belanger & Harrington, 2008; Meeuwisse, Severiens & Born, 2010; Miliszewska &
Sztendur, 2010; Nielsen, von Hellens, Greenhill & Pringle, 1997; Severiens & Wolff, 2008)
have focused on it. James et al. (2008) went one step further than many by specifically
investigating equity in higher education for indigenous Australians. The increase in the
number of students enrolled in universities outside their home country, where English is the
lingua franca, adds another level of complexity to their experience as international students
and has been considered a factor in attrition (Andrade, 2006; Beasley & Pearson, 1999;
Kaspar, 1997; Ramsay, Barker & Jones, 1999).
30
Ethnicity and cultural background are aspects of a person’s individual characteristics which
are likely to influence them when they consider, or even whether they consider, higher
education, and may also affect their choices and performance. Barrow et al. (2009) and Deng,
Lu and Cao (2007) identified the ethnic background of their subjects by: asking how they
identified themselves (Barrow et al., 2009); and what language they spoke at home (Deng et
al., 2007). Barrow et al. (2009) found that at the University of Sussex in the U.K. people
identifying as Bangladeshi, Black or Chinese had statistically lower than average results,
while research done at the University of South Australia found students who spoke Chinese at
home were less likely to drop out (Deng et al., 2007), suggesting circumstances and context
also play a part. A clash between the culture of the institution and ethnic background of the
entering students can also cause conflict which is not readily recognised and may result
simply in feelings of unease which the student is unable to explain (Read, Archer &
Leathwood, 2003). Read et al. (2003, p261), for example, point out that the dominant
discourse in academic culture in the U.K is one in which the student is presumed to be
“white, middle-class and male”. This could be said to be true of other western industrialised
countries such as North America, Australia and New Zealand in which all of the institutions
have been influenced by British culture and attitudes.
2.2.2.5 Socio-Economic Status
Those with lesser means are also being recognised as having specific concerns, because
attaining a higher education has been made possible for a far wider range of people (Bailey &
Borooah, 2007; Christie et al., 2004; James et al., 2008; Miliszewska & Sztendur, 2010).
Marandet and Wainwright (2010) recognised the difficulties faced by single women with
dependent children who are trying to improve their prospects by attaining a degree while
Bean (1980, 1983) used marital status as a means to exclude students from his studies.
Although more recent studies do include issues of “family” or “caring” responsibilities, these
are often part of a category such as the one suggested by Long, Ferrier & Heagney (2006,
pvi) who group “full-time work” with being “the main care-giver for children or someone
else while they were studying.”
Although parents may want to encourage their children to attain a higher educational
qualification, financial concerns are likely to affect the choices that can be made (Ozga &
Sukhnandan, 1998) and it must be recognised that having a low socio-economic status (low-
SES) or background has been used in the past as a means to discriminate against a significant
31
proportion of the population and it continues to prevent their proportional representation in
higher education (James et al., 2008). Their exclusion from higher education, and all the
benefits that can be derived from social mobility, has appeared to be a natural consequence of
poverty and assumed inferior intellect. Even when a more liberal view began to prevail,
students who hailed from less than salubrious beginnings were seen as ‘at risk’ and those
contributing most significantly to increasing attrition (Christie et al., 2004; Thomas, 2002;
Tight, 1998). Despite some arguing to the contrary, low-SES and the negatives assumed to
accompany it continue to be regarded as a significant contributor to attrition from higher
education.
The financial concerns of low-SES students can be alleviated, to some extent, by government
schemes to make a limited amount of funds available to those found to be in most need
(Cabrera, Castaneda, Nora & Hengstler, 1992; Long et al., 2006; West, Emmerson, Frayne &
Hind, 2009; Yorke & Thomas, 2003). The Australian government has recently made the
attraction of a larger number of low-SES students a priority for universities (Devlin &
O'Shea, 2011). This has resulted in the implementation of schemes such as the “In2Uni”
program following the recommendations made in the Bradley Review (Bradley et al., 2008).
Although many of these newly-targeted low-SES students may be eligible for government
financial assistance, part-time work is often required to supplement this financial support.
Taking on a part-time job while studying full-time may appear to be the solution but it has
been found that working more than fifteen hours per week may affect persistence (Christie et
al., 2004). Even students who are managing on financial aid or part-time work may still be
affected by the knowledge that they are accruing a substantial debt while they are studying
(Paulsen & St John, 2002; West et al., 2009). For students who do not live near the university
which they are attending, there are added costs such as transportation or student
accommodation and these two factors may also contribute to a reduction in time or
inclination to study (Long et al., 2006; McInnis et al., 2000b). In low-SES families,
immediate financial needs (West et al., 2009, p122) may outweigh future benefits and the
pressure to contribute by earning a wage may be responsible for students quitting, even
though their parents may recognise that their own lack of education has contributed to their
economic situation.
32
One mechanism for determining low-SES in the western world has been the use of postal
codes as markers for areas generally recognised to contain a high proportion of low income
people (Bailey & Borooah, 2007; Bradley et al., 2008; Christie et al., 2004; Herzog, 2005;
James et al., 2008; Paulsen & St John, 2002; Yorke & Thomas, 2003). This method can,
however, be flawed or biased (Bradley et al., 2008) as not every person living in an area
defined by a certain postcode will be low-SES, and areas do change their makeup over time
so that a suburb known for being run-down and frequently vandalised can be re-born by
“gentrification”. This process involves people of means buying decrepit properties which
they renovate and this, in turn, attracts other people of means, while low-SES people continue
to live there, too.
2.2.2.6 Marital and Parental Status
Some parents do attempt to remedy their financial situation and to improve their employment
prospects by entering higher education as mature age students. Marandet and Wainwright
(2010) specifically investigated this issue of parents enrolled in university to understand their
motivations and determine whether universities accommodated students with dependents.
Their research found that administrative and teaching staff did not readily acknowledge or
recognise the difficulties experienced by mature age students with children, and university
systems and services did not accommodate their needs either. In addition to this Gerrard and
Roberts (2006) found that the plight of single parents, especially single mothers, who were
experiencing financial hardship while studying, not only impacted upon themselves but may
have had detrimental effects on their children at the time of the study and which would
continue to impact them into the future.
2.2.2.7 Study Load
Despite all these additional characteristics being considered, the majority of studies
conducted on student attrition have concentrated on those who have a full-time study load
(Braxton et al., 2000, p572; Christie et al., 2004, p621; Crisp et al., 2009, p930; Harrison,
2006, p379; Price et al., 1992; Stratton, O'Toole & Wetzel, 2008, p322). Undergraduate
students are typically the target group for studies of attrition (ACER, 2010; Astin, 1975;
Bean, 1980; Bennett, 2003; Blanc, DeBuhr & Martin, 1983; Davidson, Beek & Silver, 1999;
Fitzgibbon & Prior, 2003; Johnson, 1994; Martin, McLachlan & Karmel, 2001; McInnis &
Hartley, 2002; Ozga & Sukhnandan, 1998; Pascarella et al., 2011; Seymour & Hewitt, 1997;
33
Spady, 1971; Tinto, 1975; Yorke, 1998), while postgraduates have been considered by some
(ACER, 2010; Andrews, Schinke & Da Costa, 2001; Cohoon, 2007; Pyke & Sheridan, 1993).
A combination or any one of the demographic characteristics discussed in the preceding
sections may result in a new student having necessarily to enrol on a part-time basis. It may
be that their socio-economic status does not allow the student to devote themselves solely to
study, or they may be responsible for the care of dependent children or an elderly parent, or it
may be that a combination of these or other factors might necessitate part-time study. It has
been de rigueur for studies of attrition to exclude part-time students as it was believed that
understanding the process of quitting would be facilitated by concentrating on students who
were assumed to be devoting all of their time to study, whether they were at home or in class,
and only more recently has it become clear that the accepted pattern of enrolment has
changed (James et al., 2010; Krause et al., 2005; McInnis et al., 2000b). Much of the
influential work on attrition has emanated from the U.S. where certain conditions are
imposed by researchers, one example of which has been the exclusion of students who do not
have a full-time study load (see, for example, Bean, 1980, 1983).
2.2.3 ICT Degrees
In the preceding section the focus was on the study of attrition as a field of research. In this
section the research undertaken to understand attrition, specifically as it pertains to ICT
degrees, will be discussed. The focus on ICT degrees is necessitated by the fact that this
research is part of the larger ALTC-funded project with a remit to study ICT. This will
necessarily be a brief discussion as much of the work relating to attrition in ICT degrees has
been undertaken to understand the culture of computing and the teaching of ICT.
Additionally, Information Technology as a Field of Education (FOE) was not recognised as a
new category in government statistics until 2000 (Krause et al., 2005) so the study of
attrition, generally, and information about the loss of students from degrees classified by the
government as “IT”, can only provide a narrow window through which to view this problem.
As the existence of computers is a relatively new phenomenon it can be said that computing
as an academic discipline is pre-paradigmatic (Kuhn, 1970). Initially, it seems, the teaching
of computing was not considered problematic because attrition was viewed as an issue for
individual students, not the course or the university. Since ‘computer science’ has been
34
presented to students as an inherently complex field containing much abstraction, teachers in
high schools and universities appear to have attributed failure to the students’ lack of ability
to grasp the concepts and wrestle with the logic. These sorts of criticisms had also been
levelled at university students, in general, at the start of the 20th century as William Mather
Lewis, President of Lafayette College, made clear in an article published in 1928. In that New
York Times article, Lewis (1928, p137) indicated that other college leaders of his time
believed that it was their duty “to make available for the student the best possible curriculum
instruction and equipment. If he fails to take advantage of them, that is his own affair” as
“[h]e is a college man, not a school boy, and must learn to stand on his own feet.” It appears
that this attitude remains current for teaching staff in Australian universities (Taylor &
Bedford, 2004). Despite the principles for understanding attrition having been, in a general
sense, determined, the research on attrition in ICT courses is still at an early stage. It is also
difficult to make comparisons between countries because what constitutes a successful
student outcome varies. In the UK, for example, taking Honours is part of the process in
attaining a three year degree (Boyle, Carter & Clark, 2002). In contrast, taking Honours in
Australia is additional to a three year degree. Nevertheless, as the literature on attrition of
students (i.e. those not trying to distinguish differences between males and females) in ICT
courses is relatively rare, any research undertaken in this area will be given some
consideration.
Several authors have revealed issues which are likely to be fundamental problems common to
teaching computer science in many parts of the world. Beaubouef and Mason (2005)
identified poorly designed laboratory classes as a core problem. Laboratory classes are
intended to be an opportunity for students to practice programming and get help and feedback
from their tutor but Beaubouef and Mason’s (2005) investigation found this was not the case.
They cite Walker’s (2004) critique which makes the point that laboratory classes tend to
become programme de-bugging sessions where one student’s problem can take all of the
tutor’s time. This may be due, in part, to the common practice of assigning the teaching of
introductory programming courses to graduate students. As these graduate students often
have no formal education in the practice of teaching (Beaubouef & Mason, 2005) it may be
easier for them to become distracted by one student’s problem to the detriment of other
students waiting for the tutor’s help. The combination of large classes, inexperienced
35
teachers, and the time-consuming nature of the subject, all conspire to give students the
impression that programming, and therefore ICT, is not for them.
Research from Finland (Kinnunen & Malmi, 2006), the US (Barker et al., 2009; Beaubouef &
Mason, 2005; Biggers, Brauer & Yilmaz, 2008), and Australia (Sheard, Carbone, Markham,
Hurst, Casey & Avram, 2008) all support this contention as they identify the focus on
programming in introductory computer science and computing courses as being detrimental
to a student’s view of the field. Kinnunen and Malmi (2006), for example, found students
were unimpressed with the amount of time needed to complete programming assignments
versus the payoff of passing the course. Biggers et al. (2008) also identified this view of
programming held by students who indicated that they believed the workload was tedious,
boring and not worth the effort. Not only can the focus be quite myopic in the early stages of
the course, but there are also assumptions made about the level of previously acquired
knowledge and skill of students beginning computing courses such as: expected mathematics
level; problem solving ability and project management skills (Beaubouef & Mason, 2005);
exposure to and competency in programming (Biggers et al., 2008); and prior knowledge of
computing (Barker et al., 2009; Kinnunen & Malmi, 2006; Sheard et al., 2008).
This is not to say, of course, that students are without fault or that the advice they received
prior to enrolling in computer science or a computing course was flawless. Kinnunen and
Malmi (2006) found that students did not have a clear understanding of what studying
computer science entailed and identified “lack of time” as a major issue. Further investigation
revealed that students had: enrolled in too many courses; underestimated the time required to
do exercises; and that outside factors such as work, family commitments or their hobby had
intervened (Kinnunen & Malmi, 2006). Beaubouef and Mason (2005) also cited students’
lack of understanding about computer science as a factor in their deciding to quit but found
that, often, the advice students had received about suitable courses was based on the advisor’s
lack of knowledge.
However attrition occurs, Figure 2.1 (using data purchased from DEEWR (2011) on the
government category Information Technology degrees – see Appendix A) shows that it
ranged from 5,679 in 2001 to 3,096 in 2008 of commencing ICT students. As the number of
36
commencing students dropped from a high point of 32,444 in 2001 to a low point of 17,420
in 2008 (see Appendix B), the attrition numbers also declined.
Figure 2.1 Commencements & attrition of students in Australian "IT" degrees 2001-2008 (DEEWR, 2011)
Figure 2.2 shows, however, that as a percentage of commencing students, attrition has ranged
between 16.5% and 18% during that period. For example, in 2001 total commencements were
32,444 and attrition was 17.5%. In 2007 commencements were 17,420 (i.e. almost half of
2001), yet attrition was actually higher at 18%. On average then, despite the considerable
drop in commencements, the rate of attrition has remained consistent, demonstrating that it is
an issue of concern.
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Attrition of Students Commencing Students
37
Figure 2.2 Attrition % of commencing students in Australian "IT" degrees 2001-2008 (DEEWR, 2011)
2.3 The ICT Discipline
The term ICT has been used to describe Information and Communication Technology
(Mulder et al., 2002, p1; Webb, 2002, p237). ICT is not a universally defined concept,
however, as the meaning attributed to the initials varies (e.g. Information, Communication
and Technology (Murray, 1998)). Although the variations on what the initials “ICT”
represent do not change the underlying meaning, they demonstrate that there is a fundamental
problem caused by the lack of an agreed and universal concept that can be attributed to the
initials ICT as the term is frequently treated as synonymous with Information Technology or
IT.
Having said that, several authors have defined what ICT (or, in most cases, simply IT)
encompasses in various ways. Buckingham, Hirschheim, Land & Tully (1987, p4) state that it
is comprised of “hardware, software, data, analytic methods, and telecommunications”.
Watson and Myers (2002, p259) describe it as a “combination of information, computing and
communication technologies”, while Lynch (2007) presents a more comprehensive
definition:
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Attrition %
38
“Artefacts – such as computers, computer software, computing accessories,
handheld digital computing and communications devices, computer networking,
and digital communication infrastructure – and the knowledge, processes and
techniques that inform the development, implementation and servicing of these
technologies”. (Lynch, 2007, p2)
The Australian Research Council (ARC, 1998, p6) states that ICT comprises the “sub-
disciplines of computer science, software engineering, computer engineering, digital
communications and information systems”. It should be noted, however, that this definition
of ICT is narrower than that found in industry or education.
The lack of clarity in defining an ICT degree (Craig, 2010, p14) demonstrated here may be
the result of its being a relatively new educational field (Craig, 2010) compared to law,
medicine or engineering, for example. In Australia, university courses on programming and
the use of computers were offered for the first time in 1956 and over the following thirty
years computing departments were established in all Australian universities (Craig, 2010).
Initially, these departments were housed in the faculties of Engineering, Science or
Mathematics and, more recently, in the faculties of Commerce, Information Technology or
Informatics (Craig, 2010). As the use of computers has changed dramatically over the past
seventy years: moving from the domain of the few, into industry, and then into the home, the
guidelines established by many professions (e.g. Medical, Engineering, Law) over longer
periods of time were not in place as the increasing need for education and training became
apparent. As a result of both the rapid increase in the need for educated computing
professionals and the way in which computing departments were established, most education
and training institutions have defined what ICT comprises in different ways.
A demonstration of this can be made by examining the positioning of IS in the Faculties of
the four universities participating in this research: University of Queensland (UQ), Swinburne
University of Technology (SUT), the University of Wollongong (UOW) and Murdoch
University (MU). The intention, here, is to provide a small set of results that demonstrate
where each university has placed one field of the ICT discipline, as an example of the
diversity of approaches taken to determine the location of ICT within each university’s
39
structure. The words “information systems” were typed into the search facility of each
website with the following results:
• UQ: three degrees: ‘Bachelor of Commerce’ (Faculty of Business, Economics and
Law), ‘Bachelor of Information Technology’ (Faculty of Engineering, Architecture
and Information Technology), and ‘Bachelor of Science’ (Faculty of Science);
• SUT: one degree: ‘Business Information Systems’ (Faculty of Information and
Communication Technologies);
• UOW: three degrees: ‘Bachelor of Business Information Systems’ (Faculty of
Informatics), Bachelor of Commerce’ (Faculty of Commerce), and ‘Bachelor of
Information Technology’ (Faculty of Informatics);
• MU: two degrees: ‘Applied Information Systems’ and ‘Business Information
Systems’ (Faculty of Law, Business and Information Technology.
As these results show, Information Systems (IS) is taught in a variety of faculties and the
disciplines combined to create faculties are also diverse. MU has a joint faculty of law,
business and IT, UQ has a faculty that unites engineering, architecture and IT and UOW has,
in 2013, just combined its Engineering and Informatics Faculties. Similar variations in the
kinds of ICT degrees offered exist in all of the 37 public universities Australia-wide, so it
becomes clear that the content, and context, of an ICT degree varies significantly from one
institution to another.
In order to make comparisons with these universities the definition of an ICT degree, for the
purposes of this research, was defined in Chapter 1 as one that comprises any or all of the
following: electrical engineering, computer science, computer systems engineering, software
engineering, computer engineering, information technology, telecommunications engineering
and information systems within a university.
2.3.1 Problems Affecting the ICT Discipline
One of the major challenges faced by the ICT discipline at the present time is the lack of ICT
professionals which is caused by three problems:
• the high rate of retirement (Crisp et al., 2009);
• the lack of enrolments (Cory et al., 2006; Granger et al., 2007; Lewis et al.,
2007; Zweben, 2008); and
40
• the high rate of attrition (Bailey & Borooah, 2007; Marks, 2007).
As Crisp et al. (2009, p924) point out, the Baby Boom generation are retiring and will
continue to do so in the near future, with many leaving their positions in ICT, thereby
creating a large number of vacancies. However, this abundance of opportunities will be
wasted if there are not sufficient people trained to take their places.
Statistics purchased from the Australian government (DEEWR, 2011) show that ICT
commencements have declined (see Appendix B) and even if every student who began an
ICT degree persisted, they would not fill the predicted increase in the number of vacancies
for qualified graduates in the Australian ICT industry as demonstrated by the ACS (2008, p7)
in their ICT Skill Forecast Project report. In that report the trend of graduates versus retirees
to 2020 is depicted, together with four potential scenarios:
Scenario 0: Unrestricted migration growth and education decline;
Scenario 1: Migration capped, and education levels held;
Scenario 2: Migration capped, education increased;
Scenario 3: Migration capped, education increased, migration loss stemmed (ACS 2008,
pp28-34).
With Scenario 1 termed “most likely” and whose outcome “indicates the shortage growing by
29% by the year 2010 to over 14,500 FTE, worsening to 19,000 by 2015, and then to over
25,000 by the year 2020”, and Scenario 3 termed “best case” in which student graduate
numbers increase at 12.5% per annum from 2007 levels, and expected outward migration of
graduates reduces, resulting in a predicted “glut” of 778 graduates without a job (ACS 2008,
pp28-34). What will actually happen is somewhere in between.
It should be noted that the ACS (2008) report included the effect of migration and economic
growth trends on the overall predicted results, whereas Figure 2.3 depicts the actual
commencement rates for all students undertaking an Information Technology degree between
2001 and 2008. These figures use the government category “IT” but all other discussion will
use “ICT”.
41
Figure 2.3 All commencing students in Australian "IT” degrees 2001-2008 (DEEWR, 2011)
Although this research is focussed on attrition of students as a homogenous entity, it is vital
to examine all the data gathered about these students by gender since a sub-problem
associated with the 2nd and 3rd problems indicated at the beginning of this section (i.e. lack of
enrolments and high attrition) is the lack of engagement of women in the ICT discipline,
which has been a concern since the mid 1980s (Craig, 2010). This lack of engagement is
reflected in low enrolments of women in ICT degrees (Faulkner & Lie, 2007; Galpin, 2002;
Gras-Velazquez et al., 2009; Henwood, 2000; Lang, Craig, Fisher & Forgasz, 2010; Lasen,
2010; Lewis, McKay & Lang, 2006b; Misa, 2010).
The investigations into the dropping enrolments of women began in the 1980s (Dweck, 1986;
Jagacinski, Lebold & Salvendy, 1988; Kimball, 1989; Manis et al., 1989) and continued for
the intervening two decades (Badagliacco, 1990; Beyer, Rynes, Perrault, Hay & Haller, 2003;
Kramer & Lehman, 1990; Krause et al., 2005; Margolis & Fisher, 2002; Young, 2000) with a
variety of possible explanations being considered. In more recent times it has become clearer
that the issues thought to be women-specific and requiring a solution such as a gender-
inclusive curriculum (Koppi, Sheard, Naghdy, Edwards & Brookes, 2010) might not be
appropriate (Blum & Frieze, 2005; Frieze, Quesenberry, Kemp & Velazquez, 2011) and that
curriculum changes ought to improve the learning and teaching experience for all students
(Devlin, 2013; Margolis & Fisher, 2002).
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42
Figure 2.4 demonstrates the necessity of approaching student attrition by considering females
separately from males as it represents the same statistics as those in Figure 2.2 but the
commencing student data is split by gender. The lack of women entering university to study
“IT” is evident in these data as the highest number of females commencing a degree was
8,548 (26.3% of the total) in 2001 and the lowest number was 3,347 (19.2% of the total) in
2007.
Figure 2.4 All commencing students in Australian "IT" degrees 2001-2008, by gender (DEEWR, 2011)
The subsequent reduction in the number graduating, as a result of attrition, contributes to the
noticeable dearth of females in the ICT industry (Gras-Velazquez et al., 2009; Logan &
Crump, 2007; Moon, 2007).
2.3.2 Women Undertaking ICT Degrees
Compounding the problem of the loss of students from ICT study at university, with attrition
rates of women equal to those for men (Cohoon, 2001), is the noticeable drop in numbers of
both women and men enrolling in ICT degrees, as Figure 2.3 demonstrates. This is
acknowledged by Lewis, Lang and McKay (2006a) who recognise that there is both a general
downturn in interest in ICT and that this is coupled with shrinking numbers of women
enrolling and remaining in ICT degrees in Australia as shown in Figure 2.5.
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43
Figure 2.5 Attrition of commencing female students in Australian "IT" degrees 2001-2008 (DEEWR, 2011)
Despite the fact that the loss of women from the study of ICT at universities has been
recognised and documented since the mid 80s, a large body of research into the reasons for
this has not been created. Although researchers (Badagliacco, 1990; Bebbington, 2002;
Bystydzienski & Bird, 2006; Dingel, 2006; Faulkner & Lie, 2007; Gras-Velazquez et al.,
2009; Lewis et al., 2007; Muller, 1997) have speculated about why women who enrol in ICT
degrees do not persist, relatively few have actually asked them to explain their decision and
what lead them to quit (Katz, Allbritton, Aronis, Wilson & Soffa, 2006; Roberts, McGill &
Hyland, 2011b; Seymour & Hewitt, 1997; Thompson, Barker, Powell, Brawner & McKlin,
2012; von Hellens & Nielsen, 2001). Instead, researchers have posited a variety of theories to
explain why women’s participation in computing has declined which Crump, Logan &
McIlroy (2007, p350) summarised as “sex role conditioning and stereotyping; the perception
of computing as the domain of ‘geeks’ and ‘nerds’; the lack of a critical mass of women in
ICT and the rate of change in the industry, which makes it difficult for women to re-enter
after a break for childbearing and rearing”, and which Misa (2010, p5) states is
“unprecedented in the history of the professions.”
Researchers have identified a number of other factors external to universities that may have
contributed to the overall decline in ICT enrolments, as well as being issues that could have
been more detrimental to women. Becerra-Fernandez, Elam & Clemmons (2010) identified
the internet bubble burst, the technology stock market crash and the move to off shoring of
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2.5 Commencements & Attrition of Female IT Students 2001-2008
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44
ICT jobs, as did Craig (2010, p35), Katz et al. (2006) and Kelan (2008) while Blomqvist
(2010, p133) stated that the decline began before the “dot com crisis”. Roberts, Kassianidou
& Irani (2002, p85) support this claim by revealing that academic institutions decreased their
intake of computer science students when they were unable to hire sufficient faculty to meet
the increasing demand for places during the 1980s. To achieve this reduction, academic
institutions made the entry requirements more stringent, increased the level of mathematical
achievement for new admissions, and ramped up the level of complexity in introductory ICT
courses to filter out many of those who had gained entry (Roberts et al., 2002). As Roberts et
al. (2002, p85) point out; this deliberate strategy to limit places is likely to have a
“disproportionately negative effect on enrollment (sic) by women and minorities”. It is also a
hidden process which does not seem to have been discovered by other researchers who have
investigated the reasons for the decline in ICT enrolments, and supports the contention that
this sort of activity, intended for one purpose, has unpredicted and harmful effects (Cukier,
Shortt & Devine, 2002; Lewis et al., 2007). It also means that claims of declining interest in
ICT (Cory et al., 2006; Craig, Fisher & Lang, 2007; Lasen, 2010; Lewis et al., 2006a; Misa,
2010; Pearce & Nakazawa, 2008; Poster, 2012) might be re-examined as it may be that many
wanted to enrol but were prevented from doing so by the increased demands imposed on
prospective ICT students.
The previous Figure 2.4, showing the number of commencing students by gender, and Figure
2.6 support the claim that few women are enrolling and remaining in ICT degrees in
Australia:
45
Figure 2.6 Attrition % of commencing students in Australian "IT" degrees 2001-2008 by gender (DEEWR, 2011) It should be noted that for all but one year (2004) attrition of female students as a percentage
of commencing students was lower than that of male students. However, the lower number of
women commencing ICT degrees means that an attrition rate ranging from 15.6% to 17.3%
results in a more noticeable lack of female students i.e. in a class containing 6 female students
the loss of one sixth of that number results in a drop to five female students.
2.4 Attrition Reduction Strategies
The previous sections have described many of the factors which are thought to influence a
student’s decision to quit. What is now required are effective strategies to address these
factors. Australian researchers McInnis et al. (2000a, p41) acknowledge the necessity of this
approach by specifically stating that “identifying successful strategies should be a research
priority.” This section presents strategies targeting female ICT students and then moves on to
those which have been found to have the most potent effect on university cohorts more
generally.
Lewis et al. (2006b) note that the drop in numbers of female ICT students is in stark contrast
to women’s overall participation in higher education which has reached an approximate ratio
of 4:3 with men (James et al, 2010, p67). In response to the recognition that a remedy must
be found, these authors established a research project they named “Women in Technology –
14
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46
Swinburne” (WIT-S) (Lang, McKay & Lewis, 2007; Lewis et al., 2007; Lewis et al., 2006b)
so that they could acquire a deeper understanding of the part played by gender in the
participation of students in ICT education at Swinburne University of Technology. The
research project was influenced by the success of the interventions undertaken in the School
of Computer Science at Carnegie Mellon University in the U.S. (Margolis & Fisher, 2002).
As a starting point, a network for female students called the Women in IT @ Swinburne
(WIT@Swinburne) network was created. According to Lewis et al. (2007) the
WIT@Swinburne network promotes a variety of events designed to inform and assist new
and current female ICT students by enabling them to recognise, understand and transform the
prevalent gender discourses in their Faculty and peer groups by challenging the common
beliefs and stereotypes that are typical of ICT. This, the authors believed among other
positive results, would improve female ICT students’ confidence, give them a sense of
belonging and foster a more inclusive environment, thereby reducing female student attrition.
Another way to improve confidence and give female students a greater sense of belonging is
the use of role models. Both Roberts et al. (2002) and Black, Curzon, Myketiak & McOwan
(2011) have demonstrated the positive outcomes derived from having female role models,
who are at different points in their studies or careers, that present a "stepping-stone model"
(Roberts et al., 2002, p86) to the students. There are indications that this has been successful
in America, where the annual Grace Hopper Celebration of Women in Computing attracts
high school and university students from across the country (Anita Borg Institute for Women
and Technology, 2012). This gathering gives both potential and current female students
opportunities to learn about women who made considerable contributions to the advancement
of ICT in the past and to hear from women with highly successful ICT careers.
There is support for the belief that current students are not well informed about the wide
variety of careers available to graduates (Beyer et al., 2003; DCITA, 2006; von Hellens &
Nielsen, 2001) and that potential students may have decided against a career in ICT due to
enduring negative perceptions:
• that it would mean sitting alone at a keyboard all day long (Biggers et al., 2008; Dee
& Boyle, 2010; Frieze, 2005);
• that it is men’s work (Clayton, von Hellens & Nielsen, 2009; Crump et al., 2007; Dee
& Boyle, 2010); or
47
• that there are few jobs left since the ‘dot com crash’ (Blum & Cortina, 2007; Craig,
2010; Katz et al., 2006).
Countering concerns such as these can be achieved by ensuring that unambiguous
information about the variety of roles across all commercial enterprise is clearly conveyed to
all students. One of the ways in which this can be achieved is by citing role models, as was
suggested earlier, or by establishing mentoring relationships with academics or members of
the ICT industry.
Connecting all students to academic mentors would be beneficial as they would provide
additional support and encouragement when students begin to feel overwhelmed by their
commitments and start to question whether their performance is acceptable, or if they have
chosen the right direction (Lee & Bush, 2003). A mentor from industry, on the other hand,
would not only be able to keep their mentee on track but could also provide them with
insights about their own job and workplace (Muller, 1997), giving students an opportunity to
grasp the bigger picture and see how their skills will fit into the real world of work after they
graduate (McInnis et al., 2000a). A third type of mentoring, which is most beneficial in
assisting students with the transition to university life and negotiating through the tertiary
education environment, is that of their peers. Connolly and Murphy (2005) suggest that
having an appropriate peer mentor can provide students with a friendly face, someone they
can to speak about their concerns who possesses insider knowledge and can encourage them
when they feel doubtful.
However, all that encouragement may go for nought unless specific attention is paid to the
content and methods used in teaching computing. As Mahony and Van Toen (1990) point
out, the way in which teaching is conducted is viewed by many as unproblematic, while they
would argue that the opposite is true. They suggested that the fairly recent establishment of a
recognisable canon in these new subjects was a major cause of the increased gendering of
professional computing. Some researchers (Hanappi-Egger, 2012; Heemskerk et al., 2005;
Koppi et al., 2010; Mills, Ayre & Gill, 2008) have advocated the need for change to the
teaching canon in order to create a more ‘female-friendly’ or ‘inclusive’ environment as they
recognise the effects the male dominant view has on the curriculum. Others (Beyer et al.,
2003; Blum & Frieze, 2005; Boivie, 2010; Craig, 2010), however, warn that this may have
48
the opposite effect as the redesign of the curriculum simply reinforces existing stereotypes
about the ‘natural’ interests and behaviours of women.
One notable and well documented (Blum & Cortina, 2007; Blum & Frieze, 2005, 2003;
Frieze, 2005; Frieze & Blum, 2002; Frieze, Hazzan, Blum & Dias, 2006; Frieze &
Quesenberry, 2013; Frieze et al., 2011; Margolis & Fisher, 2002) program designed to attract
female students was established in the School of Computer Science at Carnegie Mellon
University. Its creators, Margolis and Fisher, were aware of the low numbers of women
enrolling in the School and believed that changes at an institutional level were required to
redress the disparity in numbers of women and men. Their interventions encompassed:
changing the way in which prospective students were assessed; dropping the prerequisite for
programming experience; and running workshops for high school Advanced Placement
Computer Science teachers not only to teach them C++ programming but also to make them
aware of the possibly biased treatment of girls and boys in their classrooms (Blum & Cortina,
2007; Margolis & Fisher, 2002). These measures, together with the establishment of the
Women@SCS Advisory Council, had a dramatic effect on the intake of women students
which was approximately 7% (7 out of 96) in 1995 and increased to 38% (50 out of 130) in
1999 (Frieze & Blum, 2002).
It was their recognition of a number of factors which had initially lead to the decrease in
women enrolling and completing Computer Science degrees at Carnegie Mellon University
(CMU), that allowed Margolis and Fisher (2002) to identify what was required to rectify the
situation. Although a number of authors (Badagliacco, 1990; Grundy, 1996; Jagacinski et al.,
1988; Kramer & Lehman, 1990; Mahony & Van Toen, 1990; Manis et al., 1989; Perry &
Greber, 1990) had identified reasons for the dropping interest and persistence in computer-
related study, by women in particular, in the years before the changes instigated at CMU
were undertaken, Margolis and Fisher’s (2002) book brought much of that thinking together
and to bear on the lack of women in computing. Their work presents psychological insights
into the social construction of boys and girls which inculcates society’s accepted views and
behaviours in children from birth, thus imbuing them with a set of beliefs that make certain
attributes of females and males appear ‘natural’ and ‘common sense’ (Margolis & Fisher,
2002).
49
Having identified these fundamental issues about the impact of the socialising process, and
how this imposes certain beliefs on growing children, Margolis and Fisher (2002)
demonstrated how it convinces young girls that they do not have a place in science,
mathematics and technology. Margolis and Fisher (2002) recognised that this belief could be
reinforced by school teachers who unwittingly support boys in learning about computers
while sidelining the girls in their classes. To highlight this issue, together with other teaching
practices which might result in turning students away from ICT, Blum and Cortina (2007) ran
a Summer School at Carnegie Mellon University for high school teachers in which they
learned C++ programming and techniques to improve their students’ classroom experience.
This, together with the pre-1999 changes to the admission requirements and the removal of
certain candidate prerequisites, changed the profile of potential SCS candidates. By widening
the attributes of candidates to include those with leadership potential and a desire to
contribute to their community and removing programming as a prerequisite (Margolis &
Fisher, 2002), the School of Computer Science was able to quintuple its intake of female
students in 5 years (Frieze & Blum, 2002). This increased intake of female students, post
1999, has made an appreciable difference to the culture of the School of Computer Science
(Frieze et al., 2011). As Frieze et al. (2011, p430) put it, this cultural change has resulted in
“old stereotypes ... giving way to new identities, rich in breadth and diversity” which has
challenged Margolis and Fisher’s (2002) belief in the stereotypical gender divide. According
to Frieze et al. (2011), new female students encountering this inclusive culture feel that they
fit in both academically and socially, despite the highs and lows in their confidence during
their years of study, and they persist until they graduate.
Having considered several strategies that could specifically assist females enrolled in ICT
degrees, the rest of this section will consider those that address attrition of all students
enrolled at university. Despite the extensive research which has been concentrated on
understanding the factors leading to attrition, it has, in many instances, devolved into an
exercise in which one or more of the three central characters: the student; the teacher; or the
administration, are identified as being at fault (Cartney & Rouse, 2006; Lawrence, 2002;
McInnis & Hartley, 2002; Rickinson & Rutherford, 1996; Taylor & Bedford, 2004).
When considering their approach to assisting students, Blanc et al. (1983) instigated what is
still a revolutionary idea when they established a Supplemental Instruction (SI) program that
50
shifted the emphasis from identifying high-risk students to identifying high-risk courses.
High-risk courses, as Blanc et al. (1983) defined them, are those commonly regarded as the
difficult, entry-level courses in which student failure rates and withdrawals exceed 30 per
cent of course enrolees. Their solution was to attach the support services provided by the
university directly to each difficult course, rather than to individual students. This served
several purposes: it allowed specialists to identify ways in which courses could be modified
to improve teaching and learning; it made more efficient use of the available services; it
allowed students to learn from the modelling done by a specialist attending lectures, taking
notes and participating as “a student of the subject” (Blanc et al., 1983, p81). By integrating,
just as a student would, the specialist gained direct experience of the course and could act as a
model of how to think and talk about the content, and could demonstrate how to be proficient
in the course when they ran supplementary tutorials for struggling students. The authors
(Blanc et al., 1983) found evidence over a five year period that the SI group achieved higher
grades and were retained at a higher level than non-SI students. Seventeen years later,
McInnis et al. (2000a, pp57-58) also advocated SI as a solution for student attrition in their
comprehensive report on non-completion in both the university and vocational education
sectors in Australia.
In a similar but less resource-intense vein than SI, Cartney and Rouse (2006) advocate
learning in small groups as it is their contention that this approach will foster student
potential and promote integration, progression and retention. This may be particularly
pertinent to ICT, where programming is taught as an individual endeavour and working with
other students is viewed as cheating. This establishes a false impression in their first year that
software engineers work alone, when in reality software projects in industry require
teamwork and collaboration (Powell, 2008).
Although Cartney and Rouse (2006) believe that the increasing diversity of the student body
potentially militates against integration, they argue that sites of small-group learning may
represent one of the few points of personal contact between the student and the university.
Studies indicate that students identify social contact as a valuable component of their learning
experience (Longhurst, 1999; Sander, Stevenson, King & Coates, 2000), so, providing
opportunities for collaboration in small groups may act as a junction in the otherwise
51
fragmented experience many non-traditional and mature age students have of university life,
whilst imparting valuable skills which traditional students can take into the workplace.
A more recent strategy that recognises the need to make a personal connection between the
university administration and its students is the Strategic Retention Initiative (Brier, Hirschy
& Braxton, 2008). The SR Initiative (SRI) is intended to demonstrate institutional
commitment and investment in student well-being and persistence, and involves the Dean of
the college phoning each first-year student. Calling begins early in the students’ first semester
when academic requirements and social challenges are mounting. It is intended to personalise
the institution’s administration, foster institutional affiliation, identify problems or potential
problems while easing the student’s transition to the university and promoting academic and
social integration (Brier et al., 2008, pp18-19). Although Brier et al. (2008) acknowledge that
it is labour-intensive, they claim that the SRI has proven to be an excellent investment
because the college’s first-to-second-year rate of retention was approximately 88 percent
when it was initiated and that, in the seven years during which the SRI was implemented, the
retention rate ranged between 95 and 98 percent.
Bruning (2002), who approached the subject from a Public Relations perspective, also
advocated the establishment of a relationship between the university and its students because
research has shown that effectively managed relationships between an organisation and its
public affect key public member attitudes, evaluations, and behaviours. Bruning (2002, p42)
conducted a survey to determine whether student–university relationship attitudes and
satisfaction evaluations would identify those who would return to the university from those
who did not. Respondents were asked to indicate their level of satisfaction with their social
experiences, the recreational opportunities available, the services provided by the university,
the education they received, and their perceived value of the education provided by the
institution. Bruning (2002, p39) concluded that it is possible to identify who will return,
based on their attitudes and satisfaction ratings, and stated that this provides a quantitative
illustration of the benefits of incorporating a relationally-based grounding for the practice of
public relations in universities.
Muller (1997, p622-624) believed that students could benefit from having a personal
connection to an experienced mentor who could acquaint them with the opportunities in the
52
science, mathematics and engineering fields, offer guidance and advice based on experience,
and provide support, encouragement, and access to professional networks for further career
development. Muller’s focus was on establishing a national program called ‘MentorNet’
designed to assist the retention of women, though mentoring can be beneficial for all students
as McInnis et al. (2000a, p58) indicate in their report to the Australian government at the turn
of this century.
Another way to support entering students is recognising their educational differences, as the
days when most students enrolled directly after leaving high school have passed (James et al.,
2009). The difficulties many students experience when they find they are not equipped with
the prerequisite knowledge some courses assume they have, simply adds to the burden of
acclimatising to the university environment. Providing multiple pathways into computing
studies has been suggested (Lewis et al., 2007; Powell, 2008; Roberts, McGill & Koppi,
2011a; Roberts, McGill & Hyland, 2012) as a means to remove an unnecessary impediment
to the progress of those who do not have the expected background and skills which are part of
the existing courses. Separating students with less prior knowledge into classes that allow
them to reach the requisite level, while not holding back those with greater experience and
skills, is one way in which all students can be better served and, thus reduce the attrition
resulting from students falling behind.
Connolly and Murphy (2005), however, disagree with taking a one-strategy approach to
tackling student attrition and state that a combination of approaches is much more effective at
increasing retention than single strategies. Tinto (1993, p149) also held this view when he
advocated the need for “seven action principals” for educational institutions which involved
them providing: resources for program development; incentives for program participation;
ownership for institutional change; support for those implementing change; the means for
campus-wide collaboration; the learning of educational skills by their academic staff; and
programs for the continuous assessment of these efforts (Tinto, 1993, pp149-152). Tinto’s
(1993, p153) motivation for advocating this spectrum of activities resulted from his
recognition that “students with different reasons for leaving and possibly difference types of
students are likely to respond in different ways and at different times to different forms of
institutional action” and that “the art of successful institutional retention is to balance these
varying needs in a co-ordinated, carefully-timed program of action.” Connolly and Murphy
53
(2005, p12) suggest that combined initiatives such as adopting a welcoming approach to
students, facilitating small group tutorials, introducing innovative teaching, and establishing
learning support centres are all advantageous in increasing retention rates as these initiatives
are likely to assist student socialisation in their new environment. By ensuring that students
feel welcome, before they arrive for their first official attendance day, by making sure that
staff involved in the course choice and enrolment processes are knowledgeable and well
organised and by having processes that are not, themselves, overly bureaucratic, will reassure
students by creating a positive experience of the institution. Identifying appropriate peer
mentors can also ease the transition for new students, giving them a friendly face with which
to identify, and a person with whom to speak about concerns, who is not part of the
institution but can provide them with insider knowledge (Lee & Bush, 2003; Lewis et al.,
2007). Once the student has begun their course it is imperative that they are participants in
their own education. Passive means of teaching, such as lectures, are a didactic teaching
methodology and not one that engages students in active learning (Jones, 2007). Participating
in a small-group, dynamic tutorial environment where vital skills such as communicating,
problem-solving, synthesising knowledge and sharing ideas will ultimately develop students
into knowledgeable and well-rounded individuals (Cartney & Rouse, 2006). Despite these
measures, some students might still struggle and more individualised and intensive assistance
may be required to keep them on track and ensure these “at risk” students are given assistance
customised to address their areas of specific concern. As this multi-strategy approach
addresses social integration and aspects of the teaching and learning environment, it is more
likely to improve student perceptions of their university experience and intervene in what
might otherwise be an inevitable process of quitting for some.
Rickinson and Rutherford (1996, p218-219) also believed that a number of factors could
combine to create the tipping point for students at risk of withdrawal and advocated the
systematic monitoring of students who might be unable to adjust to the academic and social
demands of university life. Their study of that monitoring system found that it could provide:
early identification of students in difficulty; evidence of course structure issues; and a greater
insight into the admissions process. All of these have the potential to be addressed or
modified and this would reduce the anxiety and stress experienced by underprepared students
entering the university system and allowing them a greater opportunity to succeed.
54
Each of these strategies has admirable qualities and could all be regarded as taking a step
towards realising the goal of “bridging the gaps between academic, administrative and
support programs” (McInnis, 2003, p13) in what Kift (2009, p9) describes as a “whole-of-
institution transformation” which she recognises will require “a comprehensive, integrated,
and coordinated strategy ... across an entire institution and all of its disciplines, programs, and
services” (Kift, 2009, p10). As this vision is yet to be realised, the strategies discussed here
are valuable steps on the path that began with what Kift (2009, p9) termed ‘bolt on’ solutions
which are separate from the curriculum, on the way to the ultimate goal in which universities
recognise that the curriculum is vital to “mediate the diversity in preparedness and cultural
capital of entering students” (Kift, 2009, p9) and that central to that goal is the recognition
and reward of teachers whose teaching engages students in the learning process (Devlin,
Brockett & Nichols, 2009). While acknowledging efforts such as the National Teaching
Quality Indicators project (Devlin et al., 2009) this goal is yet to be fully realised in most
higher education institutions and attrition remains a potential outcome for university students.
2.5 Taking the Socio-Rational Approach to Attrition
Although attrition has been causing concern for many decades (see Lewis, 1928 as an early
example), and numerous researchers have attempted to identify why students quit their study
and posited various hypotheses, there does not appear to be one theory that unifies all the
views. For that reason, this study presents an understanding of attrition in ICT degrees in
Australia as it is seen in the socio-rational approach. This view of attrition is derived from
Tinto’s (1975) Social Integration Model and Bean’s (1980) Rational Decision Model.
Both of these models have influenced researchers investigating the causes of attrition in the
decades since they were conceived. Tinto’s (1975) Social Integration Model, for example,
has been merged with other ideas by researchers (Belch et al., 2001; Berger & Braxton, 1998;
Braunstein et al., 2001; Braxton et al., 2004; Braxton et al., 2000; Bray et al., 1999;
Ethington, 1990; Georg, 2009; Milem & Berger, 1997; Munro, 1981; Pascarella, 1980;
Woodard et al., 2001), but it was the attempted combination of Bean (1980; Bean & Metzner,
1985) and Tinto’s (1975, 1993) ideas by Cabrera et al. (1993) and Weng et al. (2010) which
allowed the socio-rational approach taken in the current research to be identified.
55
Cabrera et al. (1993) acknowledge that “only two theories have provided a comprehensive
framework on college departure decisions. These two theoretical frameworks are Tinto’s
[1975; 1987] Student Integration Model and Bean’s [1985] Student Attrition Model”
(Cabrera et al., 1993, p123). They believed that, based on their own earlier work (Cabrera et
al., 1992), the Tinto (1975) and Bean (1980) models could “be merged in explaining students'
persistence decisions by simultaneously testing all non-overlapping propositions underlying
both conceptual frameworks” (1993, p124). Their paper demonstrated that there were
structural relationships between the internal factors of social and academic integration into
the university, which were consistent with Bean’s (1985) and Tinto’s (1975) models, whilst
also finding support for the effect of external factors such as encouragement from friends and
family on the student’s persistence with study at the institution. Cabrera et al.’s (1993) use of
Bean’s theory is different to the one used in this research. Although Cabrera et al. (1993)
have claimed they used Bean’s 1980 paper, some of their referencing indicates that in fact it
was Bean’s 1985 paper on Student Dropout Syndrome. Cabrera et al. (1993) also cited both
Tinto’s 1975 and 1987 models.
Weng et al. (2010) were influenced by the work of Cabrera et al. (1993) and chose to
combine the models of Tinto (1975) and Bean (1980; Bean & Metzner, 1985) with Bandura’s
(1997) investigation of self-efficacy theory and Cabrera et al.’s (1993) integrated model of
student retention. There are, however, some valid criticisms to be made of this work from a
scholarly point of view. Although the authors begin by stating they will use Tinto’s (1975)
and Bean’s (1980) original works, combined with those of Bandura (1997) and Cabrera et al.
(1993), the citations to Bean’s work are inconsistent and it becomes difficult to be certain
which paper is being referenced in some instances. It also seems that Weng et al. (2010) have
not actually used these ideas in their entirety but picked certain aspects from some, such as
“encouragement from others, as argued in Bean’s model” and “financial attitude as included
in Bean’s model” (Weng et al., 2010, p101). The first of these is a reference to Bean’s (1980)
model and the second to Bean and Metzner’s (1985) model. They found that self-efficacy and
social integration were important factors in student persistence but, interestingly, that self-
efficacy did not predict academic integration for students studying IS in Taiwan (Weng et al.,
2010).
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Notwithstanding these previous attempts to combine the models of Tinto (1975) and Bean
(1980), this is the first time that these two models have:
• had their central concepts: social integration of, and rational decision-making by,
students applied in a socio-rational approach;
• been used to extend a reasonably comprehensive survey instrument originally applied
by two research teams in the Australian university context (West et al., 1986; Price et
al., 1992);
The fusion of the models of Tinto (1975) and Bean (1980), combined with the range of
pertinent questions posed in the West et al. (1986) survey instrument (see Chapter 3), has
allowed a hypothesis to emerge that posits that students arrive at a decision to quit because of
one or a combination of three factors. These factors are categorised in the socio-rational
approach as: unexpected events; experiences; and outcomes. The unexpected events which
have occurred to students may actually prevent them from continuing; or lead to their
decision to quit. Examples of these unexpected events include: serious illness of self; serious
illness of a family member; death of a family member; or pregnancy of self or partner.
Secondly, students may have negative experiences which impact their ability or willingness
to study. Examples of these experiences include: loneliness; not fitting in; work and study
conflicts; travel difficulties; financial concerns; hostility from people or the learning
environment; and problems with accommodation. Finally, students may not obtain the
expected benefits of attending university because of a combination of the previous two
categories, or as a result of: deficient teaching practices; an unsuitable learning environment;
or deficiencies in the student’s abilities and/or outlook. Examples of the outcomes which may
result from these include the student’s recognition that they have failed to: grasp concepts;
understand the language; complete assignments; or pass exams. Using the established models
of Tinto (1975) and Bean (1980) allows the three categories in the socio-rational approach to
be classified as either (S) Social (the experiences and unexpected events) since they emanate
from students’ social lives in and outside the university, or (R) Rational (the outcomes) which
are recognised by the student as requiring some rational thought and contemplation of the
choices and actions available to them. The need for a new approach results from this review
of the literature which has found many studies based on these ideas without explicit
acknowledgement (Braxton et al., 2000; Christie et al., 2004; Crisp et al., 2009; Harrison,
2006; Kramer, 2007; Price et al., 1992; Stratton et al., 2008; West et al., 1986).
57
To support the premise of this research: that a student’s integration into the social and
academic lives of their university has an effect on their experience, and that students do make
rational decisions based on a variety of factors they deem to be important, Tables 2.1 – 2.3
contain studies on various factors believed to affect university students without the
researchers having stated that they are investigating issues related to social integration or
rational decision-making.
The studies have been classified as (S) to indicate their main focus is social integration, (R) to
indicate their main focus is students making rational decisions or (S & R) to indicate their
focus is on both the social integration and rational decision-making of students. This
categorisation of attrition research does not indicate that all those classified as (S), for
example, are dependent upon Tinto’s (1975) work on social integration, nor does the (R)
category indicate that that research has been undertaken using Bean’s (1980) work. The intent
is simply to indicate, broadly, that research on attrition can be categorised in this manner,
though it is acknowledged that there are any number of other ways to categorise this
tremendous body of work. The focus here is to examine the research to: identify various
approaches to understanding attrition; demonstrate the utility of the socio-rational approach
in categorising previous research; and demonstrate that the socio-rational approach has not
been used to understand attrition in the way this research intends.
PF Research Summary Author(s) & Year
S Stress effects on students Bray et al. (1999) S Students’ ability to fit into university culture Read et al. (2003) S Student match to course, effects of peers, gender
composition of academe and integration of students Johnes & McNabb (2004)
S The university experience Parmar & Trotter (2005) S The effect of receiving emotional support during first
year at university Wilcox, Winn & Fyvie-Gauld (2005)
S Mastering ‘discourses’ at university Lawrence (2005) S Institutional climate as a factor in departure Rhee (2008) S Differences in social and academic integration for
minority and majority students Severiens & Wolff (2008)
S Impact of an unfamiliar learning environment disrupting a student’s confidence
Christie et al. (2008)
Table 2.1 Summary of attrition studies classified as Social (S)
58
PF Research Summary Author(s) & Year
R Financial aid as inducement to re-enrol Herzog (2005) R Student reasoning for dropping out Hermanowicz (2006) R Student perception and interpretation of academic
demands Robotham & Julian (2006)
R Differences in attitude of students leaving in first semester and those leaving in second semester of their first year
Andrew et al. (2007)
Table 2.2 Summary of attrition studies classified as Rational (R)
PF Research Summary Author(s) & Year
S & R Ability to acclimatise to the culture of the academic discipline
Ylijoki (2000)
S & R University quality of life and learning Audin, Davy & Barkham (2003) S & R Influence of financial hardship and self-esteem Bennett (2003) S & R Socio-economic status Yorke & Thomas (2003) S & R Academic and social integration Rhodes & Nevill (2004) S & R Factors contributing to a student becoming an ‘early
walker’ Bennett, Kottasz & Nocciolino (2007)
S & R Effects of student characteristics and perceptions on student-to-student and student-to-faculty interactions
Barker et al. (2009)
S & R Motivations of students with children Marandet & Wainwright (2010)
Table 2.3 Summary of attrition studies classified as both Social (S) and Rational (R)
This review of the literature has identified a number of factors which are associated with
students’ decisions to quit. Some of these factors are associated with social experiences of
students while others are associated with students making rational decisions after weighing
alternatives.
Although there have been numerous attempts to identify the factors leading to attrition using
measurable attributes such as the demographic characteristics of students, or less easily
measured psychological attributes, none have proved entirely satisfactory.
Understanding the factors, however, is not sufficient. What is required is a way to map these
factors to appropriate strategies, like those described in section 2.4. This mapping could be
facilitated by a socio-rational analysis, so the current research proposes to apply a
59
comprehensive socio-rational approach to understand the factors leading to attrition and to
identify appropriate strategies to address those factors.
2.6 Objectives of the Research
This chapter has indicated that a more comprehensive socio-rational approach might be used
to address the factors leading to attrition in Australian ICT degrees. Moreover, this approach
could be used to map strategies to address the factors leading to attrition.
Based on this review of the literature it is apparent that the extent and causes of attrition from
ICT degrees in Australia have not been comprehensively researched. Consequently, the
research question posed is “Can a socio-rational approach be used to identify the factors
leading to attrition in Australian ICT degrees and to identify appropriate strategies to address
these factors?” To answer this question the following objectives must be achieved:
1. to produce a comprehensive instrument, based on the socio-rational approach and
specific to ICT degrees;
2. to use this instrument in Australia, where the level of attrition is comparable to much
of the western world, to gather data from students that identifies the factors
contributing to them quitting their ICT degree;
3. to gather data from the literature and experts in academia and industry about strategies
for dealing with attrition, and analyse it using the socio-rational approach;
4. to map the factors contributing to attrition to the strategies identified ;
5. to validate the effectiveness of the socio-rational approach by interviewing experts
from academia;
6. to provide guidelines to reduce attrition by presenting a holistic strategy.
2.7 Conclusion
This chapter has presented a review of the literature on attrition from higher education as well
as that related to attrition from ICT degrees. In critically analysing some of the approaches to
the study of attrition, this chapter has suggested that a socio-rational approach may be more
useful than previous approaches. The review has also focussed on strategies to reduce
attrition, a topic that has been widely overlooked in previous research. Consequently, the
review has identified the six objectives of the current study. The following chapter will
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describe the methods used to achieve these six objectives and, thus, answer the research
question posed.
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3 METHODOLOGY
3.1 Introduction
The previous chapter presented a review of the attrition literature as it relates to university
study in general and to attrition in ICT degrees in particular. It identified many of the factors
leading to attrition and possible strategies that might be used to address, in part at least, those
factors. In addition, the chapter discussed the social factors, found in the work of Tinto (1975,
1988, 1993; Tinto & Pusser, 2006), and the rational factors, found in the work of Bean (1980,
1983; Bean & Metzner, 1985), and suggested that these two sets of factors could be
combined in a socio-rational approach which might offer insights into attrition. The work of
Cabrera et al. (1993) described an initial attempt to use both social and rational factors but
omitted factors suggested elsewhere in the literature, and did not propose as a socio-rational
approach, per se. The chapter then presented a more formal and comprehensive socio-rational
approach and concluded by proposing the objectives of this research.
This research intends to answer the research question: “Can a socio-rational approach be used
to identify the factors leading to attrition in Australian ICT degrees and identify appropriate
strategies to address these factors?”
To answer this question the following objectives must be achieved:
1. to produce a comprehensive instrument, based on the socio-rational approach and
specific to ICT degrees;
2. to use this instrument in Australia, where the level of attrition is comparable to much
of the western world, to gather data from students that identifies the factors
contributing to them quitting their ICT degree;
3. to gather data from the literature and experts in academia and industry about strategies
for dealing with attrition, and analyse it using the socio-rational approach;
4. to map the factors contributing to attrition to the strategies identified;
5. to validate the effectiveness of the socio-rational approach by interviewing experts
from academia;
6. to provide guidelines to reduce attrition by presenting a holistic strategy.
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3.2 Research Paradigm
The approach taken in this research is to combine both qualitative and quantitative data
collection methods and ways of analysing that data. This fits within the paradigm of mixed
methods research (Creswell & Plano Clark, 2007; Greene & Caracelli, 1997; Hesse Biber,
2010; Tashakkori & Teddlie, 2003). This research paradigm has been chosen because it
recognises that research situations are complex and multidimensional (Mingers, 2001, p243),
especially when the research involves people who are, themselves, complex and
multidimensional. Not only do quantitative and qualitative data yield different results but
their combination to more fully explain the phenomena of interest may also yield a better
result than one or the other could achieve on its own (Mingers, 2001). This is certainly true in
the case of research into human motivations where further details given in writing or in
person can flesh out what might otherwise seem to be perplexing actions or rationale that
statistics, alone, cannot explain.
Figure 3.1 is presented to give an overall picture of the process steps taken in this research,
beginning with a literature review, progressing through the data collection stages to identify
both the factors contributing to attrition and the strategies that have been used to reduce
attrition and culminating in the mapping of factors and strategies to create the guidelines for
attrition reduction.
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Figure 3.1 Process steps in this research
The methods used in the current research have been chosen to suit the individual objectives.
Objective 1, to produce a comprehensive instrument, based on the socio-rational approach
and specific to ICT degrees, was met by identifying a previously used instrument and adding
to that instrument the social integration factors identified by Tinto (1975, 1988, 1993, 2006)
in combination with the rational factors identified by Bean (1980, 1983). These factors were
then organised into a more formal socio-rational approach. Thus, Objective 1 has already
been realised in the literature review.
Objective 2, to use this instrument in Australia where the level of attrition is comparable to
much of the western world to gather data from students that identifies the factors
contributing to their quitting their ICT degree, was met by creating an online questionnaire,
statistically analysing the questionnaire data to find any trends and relationships that may be
of importance, and conducting follow up interviews to gather data from a sample of students
who quit their ICT degree before completion. By gathering both quantitative and qualitative
data from the questionnaire and qualitative data from the interviews it was possible to
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investigate student’s feelings and experiences at a deeper level and to reveal information that
could otherwise have remained hidden (Hair, Babin, Money & Samouel, 2003, p76).
Objective 3, to gather data from the literature and experts in academia and industry about
strategies for dealing with attrition, and analyse it using the socio-rational approach, was
met by conducting a literature review and also by identifying responses in the surveys of
senior academics and ICT professionals to gather information on strategies that may, in part,
address the factors leading to attrition
Objective 4, to map the factors contributing to attrition to the strategies identified, was met
by analysing the literature and using that to identify which strategy would be most effective
in addressing each factor. Initially a list was produced of strategies suggested by each group
(the academics being one and the industry members being the other). Once that analysis was
completed, it was possible to combine the strategies from the academic and industry surveys
with those identified in the literature, and the factors which those strategies would address,
into one table.
Objective 5, to validate the effectiveness of the socio-rational approach by interviewing
experts from academia, was met by interviewing senior academics and asking them a set of
five questions that sought to elicit their thoughts on the causes of attrition and whether the list
of 26 factors which received the highest positive rankings from the ex-ICT students covered
all the areas that they would have expected. The experts were also asked to comment on any
factors that surprised them or whether there were factors missing from the list before being
asked whether they believed the socio-rational approach was valid.
Finally, Objective 6, to provide guidelines to reduce attrition by presenting a holistic
strategy, was met by using interpretive analysis to map strategies to the factors. Given that
Objective 3 had identified the most frequently reported factors leading to attrition, Objective
6 identified strategies which address the most frequently reported factors. It is acknowledged
that factors which may only be reported infrequently may still be the crucial factor in an
individual student’s decision to quit his or her ICT degree. However, there are so many
infrequently reported factors that addressing all of them would consume an inordinate amount
of resources. So Objective 6 was realised by suggesting strategies which would have the
greatest overall impact on attrition.
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3.3 Context of the Research
To satisfy Objectives 2, 3 and 5, three different sets of data are required; these are,
respectively:
1) quantitative and qualitative data from students about their reasons for leaving their
ICT degrees. This data was collected by surveying Australian ICT students who had
quit their degree;
2) qualitative data about strategies currently being used to address attrition. This data
was available from the literature and from surveys conducted on experienced
academics and ICT practitioners;
3) qualitative data from experts in academia who were interviewed to ascertain their
thoughts and responses to five questions about the efficacy of the socio-rational
approach.
To collect the first data set, it was necessary to identify Australian ICT students who had quit
their degrees but this would have required the co-operation of the Registrars in a number of
Australian universities. Typically, it is difficult to get such co-operation, however the current
research was part of a larger research project being conducted by four Australian universities
funded by a grant from the Australian Learning and Teaching Council (ALTC).
Consequently, the registrars at Murdoch University, Swinburne University of Technology,
the University of Queensland and the University of Wollongong all agreed to participate in
the research and to mediate in contacting the required students.
The ALTC project also included separate surveys of the Australian Council of Deans of ICT
(ACDICT), members of the Australian Computer Society (ACS) and Advisory Board
members of the four partner universities. These surveys were quite far reaching as each
member of the ALTC project team had been assigned a specific area to investigate such as:
the Teaching-Industry-Learning Nexus (TRIL); Work Integrated Learning (WIL);
Perceptions of ICT; and the lack of women studying ICT. Each project team member
contributed a set of questions and, from those included in the ACDICT and ACS surveys,
data was obtained about strategies which could potentially be used to address factors leading
to attrition, which comprised the second data set. The third data set was gathered from
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individuals recognised as experts in their field who were likely to be able to judge the
veracity of the findings made in this research.
3.4 Methods
This section describes the methods used to achieve the six objectives of this research.
3.4.1 Candidate Socio-Rational Factors
Previous studies which have been classified here as taking either a social (S) or rational (R)
approach have indentified many factors which potentially influence attrition and have used
these factors in surveys of students. These surveys were intended to determine which of the
many potential factors were most significant. The socio-rational approach proposed here also
requires a set of candidate factors drawn from both previous approaches.
Fortunately, the study by West et al. (1986) provided an excellent starting point. West et al.
(1986), in their study of attrition in Australian universities and CAEs (Colleges of Advanced
Education), appear to have started from scratch despite decades of research on attrition in the
U.S. Although they don’t specify their reasons, one might surmise that the Australian
researchers believed that the education experience in the United States was quite different to
that in their own country. West et al. (1986) began by conducting a pilot study of Australian
students who were asked to rate 11 factors on a 7-point Likert scale to identify the most
significant factor contributing to their decision to quit. Those factors were: accommodation;
course, institution, distance/remoteness; finance; family; health; personal decision; chance
events; job; and academic preparedness. Each of these broad categories was explained by
presenting brief scenarios of the types of things that were included in the category and by
posing a number of questions (see Appendix C which contains a copy of the original survey).
Secondly, students were directed to turn to the section of the survey that focussed on the
factor that they had identified as most important, and rate the more detailed questions
presented there. West et al. (1986) concluded that the interaction between the factors the
students had identified provided “a simple explanation of withdrawal or persistence” while
acknowledging that “a search for variables that will successfully predict those who will drop
out is a waste of time” (West et al., 1986, pp152-153). This conclusion by West et al. (1986)
echoes similar sentiments by other researchers quoted earlier.
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Although West et al. (1986) did not specifically describe their instrument in terms of social
(S) or rational (R) approaches, the items can easily be grouped into those two categories with
institution, accommodation , family, health and chance events being classified as social (S)
(i.e. issues affecting social integration) and personal decisions, finance, job and academic
preparedness being categorised as rational (R) (i.e. issues involving rational decision-making)
and course, distance/remoteness being a combination of these two categories (S&R) (i.e.
issues affecting social integration and involving making rational decisions).
3.4.2 The Survey Instrument
Consideration, then, must be given to the means by which to gather information from people.
Providing people with the opportunity to give information relevant to attrition is limited to
using such mechanisms as questionnaires, focus groups, case studies and interviews, none of
which are entirely satisfactory. This is, however, an intractable problem which this study
cannot solve, but combining two of these approaches might improve the quality of the data
gathered.
Having recognised the limited choices available, it was determined to conduct extensive
research to identify survey tools that could be used to gather data from the target population
of this study. The identified tools (West et al., 1986) proved useful in a number of ways by
confirming the areas and issues which needed to be investigated, whilst also showing that
there are areas and issues not included in previous survey tools which the literature review
identified as important (see Appendix D).
As the investigation of attrition has continued, unabated, for many decades, the number of
potential factors that could contribute to students’ quitting has increased. The intention, in
this research, is to compile the most comprehensive set of factors that fit into the social and
rational categories of the socio-rational approach. To achieve this end, the following
additional Social (S) factors (Table 3.1) were identified in the review of the attrition
literature:
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Main Theme Survey Item(s) Author(s) Socialising There were no opportunities to socialise Jagacinski (1988)
Studying at university wasn’t as important as socialising with my friends
Teaching methods
The teaching methods were harsh and confrontational Cohoon (2001) The teachers didn’t explain the exercises The pace of teaching was too fast Barker et al. (2009) The teachers were not prepared Connolly & Murphy
(2005) The teacher’s knowledge was out of date Becerra-Fernandez et al.
(2010) The course content was male oriented Lewis et al. (2007) The focus was on individual activities rather than groups
Cartney & Rouse (2006), Powell (2008)
Sexism or rudeness
Students acted or spoke in a sexist manner Male staff acted or spoke in a sexist manner Male students wouldn’t let me participate Male staff didn’t encourage me to participate
Vogt, Hocevar & Hagedorn (2007)
Learning style The academic environment did not suit my learning style
Davies et al. (2004)
I felt it was unacceptable to be smart Seymour & Hewitt (1997)
There were no or few females in class Margolis & Fisher (2002) I was in the minority in my classes
Work My timetable didn’t fit with my work commitments Christie et al. (2004) Late enrolment Did you miss the beginning of your course? Bennett (2003) and
Trotter & Roberts (2006) Attendance at Open Days
Did you visit your university during Orientation Week?
Trotter & Roberts (2006)
Attendance at school functions
During your time as an ICT student, did you attend functions organised by your school/faculty/ department?
Trotter & Roberts (2006)
Health concerns of relatives
A family member died, was very ill or had a serious accident
Yorke (1998)
Table 3.1 Additional Social (S) factors
Since student decision-making can lead to a rational decision being made to quit, the
following additional Rational (R) factors (Table 3.2) were also required to ensure that the set
of factors used in this research were as broad as possible:
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Main Theme Survey Item Author(s) Teaching methods
The teaching methods were harsh and confrontational Cohoon (2001) The teachers didn’t explain the exercises The pace of teaching was too fast Barker et al. (2009) The teachers were not prepared Connolly & Murphy
(2005) The teacher’s knowledge was out of date Becerra-Fernandez et
al. (2010) The course content was male oriented Lewis et al. (2007)
Course issues The course was too competitive Grundy (1996) The course didn’t have a business focus Becerra-Fernandez et
al. (2010) The course was too theoretical Newmarch, Taylor,
Steele & Cumpston (2000)
I didn’t understand the concepts Clark & Ramsay (1990) The course lacked practical applications Becerra-Fernandez et
al. (2010) The course didn’t have a workplace focus The classes were boring Mann & Robinson
(2009) The focus was on individual activities rather than groups Cartney & Rouse
(2006), Powell (2008) There were too many assignments Barker et al. (2009) My results were not as high as I expected Connolly & Murphy
(2005) The course was poorly structured I didn’t understand the meaning of terms used in the course
Clark & Ramsay (1990)
Work My timetable didn’t fit with my work commitments Christie et al. (2004) There was conflict with my work commitments Kuh et al. (2007)
Transport My timetable didn’t fit with the transport timetable Bean (1985) Enrolment not first degree
When thinking about the year you enrolled in your ICT degree, was it the first year of enrolment in any degree?
Sheard et al. (2008)
Starting course late
Did you miss the beginning of your course? Bennett (2003) and Trotter & Roberts (2006)
I picked the wrong degree Harrison (2006), Yorke (2000)
Table 3.2 Additional Rational factors
As indicated earlier, part-time students were not considered good models in understanding
attrition and there was also an assumption that most students were of traditional age which
also meant there was an expectation that students were not also parents. To address these
shortcomings three demographic characteristics (Table 3.3) were added to the questionnaire:
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Characteristic Survey Item Author(s) Part-time load Please select whether you were a full
time or part-time student when you enrolled in the ICT degree
Windham (1994), Bean & Metzner (1985) & Lam (1984)
Age upon enrolment
Please select your age when you enrolled in your ICT degree
Marandet & Wainwright (2010),Trotter & Cove (2005)
Parental status When you were enrolled as an ICT student, what was your marital status?
Marandet & Wainwright (2010), Connolly & Murphy (2005)
Table 3.3 Additional demographic characteristics
A combination of the statements from the West et al., (1986) survey, and the additional
factors found in the current research, results in a comprehensive list of statements covering
both the social and rational factors that could contribute to attrition and these are presented in
Table 3.4 below:
Social Integration Factors Rational Decision Factors I couldn’t get help when I needed it The teachers didn’t explain the exercises There were no opportunities to socialise The pace of teaching was too fast There were too many distractions preventing me from concentrating on my studies
The classes were boring
The University staff were not friendly The teachers were not prepared The teaching methods were harsh and confrontational
The teachers’ knowledge was out of date
The teaching environment was not welcoming The course didn’t have a business focus I wasn’t encouraged to do well by the teachers The course lacked practical application Students acted or spoke in a sexist manner The course didn’t have a workplace focus Male staff didn’t encourage me to participate The course was poorly structured Male students wouldn’t let me participate The course didn’t meet my expectations Male staff acted or spoke in a sexist manner The course content was male-oriented I didn’t make friends with classmates The course was too theoretical Studying at University wasn’t as important as socialising with my friends
The course was too mathematical
The course was too competitive There were too many assignments The focus was on individual activities rather than groups
I didn’t understand the concepts
I felt it was unacceptable to be smart I didn’t have the expected background knowledge I didn’t feel I fitted in or belonged I didn’t understand the meaning of the terms used
in the course I didn’t enjoy attending classes My results were not as high as I expected There were no or few females in the classes I picked the wrong degree I was in the minority in my classes The academic environment did not suit my
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learning style Living in student accommodation was too difficult
Organising a suitable timetable, with no clashes, was challenging
I missed my family My timetable didn’t fit with my work commitments
I became very ill or was involved in an accident I or my partner got pregnant A family member died or was very ill or had a
serious accident Living away from home was too difficult Living at home was too difficult My family didn’t help me study at home Travelling to University was/is too difficult
because of distance Travelling to University was/is too difficult
because of transport I couldn’t get financial aid My timetable didn’t fit with the transport
timetable I lost my job There was conflict with my work commitments Attending University was too expensive Attending evening classes posed a security risk The University facilities were not adequate Table 3.4 All identified Social Integration and Rational Decision factors
The factors listed in Table 3.4 are presented as statements about events or experiences that
may have impacted on a student’s decision to quit. The statements were worded in such a
way that they are not loaded with assumptions, as has been the case in some previous studies.
There are, however, two where this was unavoidable: “I or my partner got pregnant” and “A
family member died or was very ill or had a serious accident”. It was felt that these two
questions should be asked, notwithstanding the potential they have to introduce gender bias,
as each could be an unexpected event in a student’s life. Table 3.4 presents the first
comprehensive set of socio-rational factors related to attrition in university degrees.
3.4.3 Data Collection
When considering the collection of data, the time scale will have an effect not only on when
but also how much can be collected. There are three possible time scales: a longitudinal
study, a case study or a cross-sectional study (Lee & Lings, 2008, p197-202). A longitudinal
study would be the most suitable choice if the focus of this study was on the long-term
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outcomes for students who withdrew from their degree and quit their university as it would
allow them to be tracked over a number of years. A case study would allow a snapshot to be
taken of one university cohort of ICT students but would not enable trends to be identified.
While each of these has its merits, it was determined that a cross-sectional study would be
used to identify the factors leading to attrition of ICT students from Australian universities
via both a questionnaire and interviews.
To that end, three sources of data are used to inform this research:
- Government data
- Questionnaires of ex-ICT students, ACDICT and members of the ICT industry
- Interviews of ex-ICT students
3.4.3.1 Government data
This data was purchased for the ALTC project from DEEWR (2011) on commencements and
attrition in ICT degrees from 2001 to 2008 (see Appendices A & B).
3.4.3.2 Data from ex-ICT students, ICT academics and members of the ICT industry
Two sets of data on ex-ICT students were obtained by gaining the co-operation of the four
universities involved in the ALTC project. Each of the partner universities was required to
supply demographic data about students whom they identified as having quit or transferred
from their ICT degree between 2005 and the middle of 2010. The data requested from each
university was to comprise:
(1) the total number of those students identified as withdrawn or transferred;
(2) the first year of their enrolment in an ICT degree;
(3) whether their enrolment was as an undergraduate or postgraduate;
(4) their gender;
(5) their status as a domestic or international student;
(6) their student load (full-time or part-time);
(7) their age; and
(8) the number who failed to meet academic standards for continuation.
It was intended to combine these data with both qualitative and quantitative data gathered
from the ex-ICT students via an online questionnaire. Quantitative data gathered in the
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questionnaire came from the rating of statements on a 5-point Likert scale while the
qualitative data was gathered through open-ended responses to questions.
Data using a 5-point Likert rating scale for statements designed to elicit quantitative data, and
open-ended questions to obtain qualitative data, from online and paper-based questionnaires
was gathered from ACDICT and members of the ICT industry.
3.4.3.3 Interviews of Ex-ICT Students
Follow up interviews of the ex-ICT students were used to check the completeness and
accuracy of the data gathered from them in the online questionnaire.
3.4.4 The Questionnaire
The questionnaire was deemed to be an appropriate method to gather information from a
large sample of respondents over a large geographical area and because the proposed socio-
rational approach contained many well-defined factors found in the literature.
The questionnaire design was formulated with consideration given to the type of question and
how respondents would provide their answers. Questionnaires are frequently used when the
following apply:
i) Information is required from a large number of respondents;
ii) The respondents are spread over a large area or are difficult to contact face-to-
face;
iii) The information required is a simple response to a well-defined set of questions.
Quantitative Data
The ex-ICT students were asked to respond to survey questions that covered: academic
preparedness; aspects of the course; aspects of the institution; aspects of the teaching and
learning environment; chance events; health; finances; travel; accommodation; and work;
together with a number of demographic questions such as gender, age, marital status,
ethnicity, language spoken at home, whether they were a domestic or international student,
whether they were an undergraduate or postgraduate, the degree in which they were enrolled,
and their parents’ gross annual income. Other relevant questions identified in the literature
were also asked such as: whether they were enrolled full-time or part-time, whether the
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discipline in which they enrolled was their first choice; whether they were enrolled for the
first year in the discipline; whether they had enrolled late; and whether they had attended
functions held by their faculty during the orientation period at their university (see Appendix
E).
In the questionnaire the mechanisms used to gather data were presented in several forms:
• as choices from drop-down menus;
• as ‘yes’ or ‘no’ answers with text boxes supplied for written explanations when the
respondent chose ‘no’;
• as 5-point Likert-scale responses to negative statements presented in three categories
(University, Course and Life);
• and as written responses to open-ended questions.
Demographic Characteristics
The majority of questions requiring a selection from a drop-down menu were of a
demographic nature covering gender, age, marital status and dependents, ethnicity, socio-
economic status, hours worked, and a text box requiring the respondent to state the language
they spoke at home. There were also questions which allowed respondents to specify: their
ICT discipline; and the year and semester in which they quit their ICT degree together with
questions which asked whether ICT was the respondents first choice, and whether their
enrolment in ICT was for the first year of any enrolment which provided discrete choices
between ‘yes’ and ‘no’ while also providing a text box for further details. In addition there
was an open-ended question asking respondents to give the name of their first choice of
degree if it was not ICT.
Three questions presented discrete choices between either male or female, enrolment as either
an undergraduate or postgraduate, and status as either a domestic or international student.
The final two demographic questions asked respondents to rank their performance both: at
high school; and in the degree before they quit. Both ranking scales were presented as
discrete choices ranging from Top 10%, Next 20%, Middle 40%, Lower 20% and Bottom
10%.
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Socio-Rational Factors
All the items in these three sections were socio-rational factors derived from the literature.
All the items were presented as negatively worded statements, e.g. “the course didn’t meet
my expectations.” Respondents were asked to rate these statements on a 5-point Likert scale
ranging from strongly disagree to strongly agree. The first survey section presented negative
statements about experiences of the University, the second survey section presented
statements about their Course.
The fourth survey section comprised three questions that were not grouped together. The first
question in this section was Question 6 which required respondents to choose between
Something about the course or Personal reasons or Personal reasons and the course as being
the main reason for their withdrawal from study. The other two questions in this section
allowed free-text responses to Questions 14 and 15. Question 14 invited respondents to
indicate which of the statements presented in the survey had been the main reason for their
withdrawal from study and allowed them to write about that experience. Question 15 invited
respondents to indicate if there were other reasons for their decision to quit their study,
especially if those reasons had not been covered in the questionnaire.
Numerical data gathered from the online questionnaire were analysed using the statistical
analysis program SPSS 17.0 to identify the main factors affecting the majority of respondents
to determine whether Australian ICT students are experiencing difficulties in their lives –
both their student lives and their lives outside university – which result in them deciding to
quit their ICT degree.
Qualitative Data
The ex-ICT students were asked to write their response to two open-ended questions. The
first one asked “From the questions about why you left/changed your degree, which had the
most influence on your decision and why?” and that was followed immediately by the second
question: “Are there any comments you would like to add about your experience, especially
if these questions did not cover something that was important to you?” These two questions
drew varied and valuable data from the respondents which, in a number of cases proved
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contradictory to the ratings individual students had given to the statements presented earlier
in the questionnaire.
Administering the Questionnaire
The website SurveyMonkey.com was considered to be a suitable vehicle for the
questionnaire, as it would be particularly suited to the cohort of ex-ICT students, who are
generally computer and Internet-literate. (Appendix E).
As Lee and Lings (2008, p279) point out, the length of a survey influences how many people
choose to complete it and since surveys are notorious for the low numbers who do respond
(Hair et al., 2003, p212; Krause et al., 2005), this was taken into consideration during the
design phase. Although Cavana, Delahaye & Sekaran (2001, p244) state that electronic
surveys also suffer from a low response rate, the easier administration and the fact that the
SurveyMonkey website provided a progress bar (see Appendix E) showing how far the
respondent had advanced through the questions and how much further they had to go to
complete the questionnaire, was seen as a better proposition than mailing paper-based
questionnaires to students.
Conducting a Pilot Study of the Questionnaire
To ensure the wording of questions and statements in the questionnaire was effective and
easily understood, a pilot study was conducted in the early stages of this research.
Students who were currently enrolled in an ICT degree at the University of Wollongong were
invited to participate in the pilot study and offered an incentive in the form of a gift card.
Twelve students responded to the invitation and were offered two possible dates and times to
participate. One of the two dates was suitable for ten of the student volunteers. Only one of
the pilot study participants was female and two of the male participants were international
students.
The pilot study participants were asked to note: any difficulties they encountered with the
online questionnaire; any problems they had in understanding the wording of the questions or
statements; any additional questions they thought would be helpful; and any criticisms they
had. The participants identified 7 issues:
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1. Students may not know their parent’s/guardian’s income;
2. Asking about school results is not applicable to international students;
3. There should be an opportunity to rate specific teachers;
4. Statements about the course should be more specific;
5. The Likert scale for rating statements should include ‘don’t know’ or ‘not applicable’;
6. The Likert scale allows more than one rating choice per statement;
7. Why have three options for questions: ‘yes’, ‘no’ and ‘no plus reason’?
The pilot study participants also offered solutions for two of the issues they identified: for
issue 1, present a range of parent/guardian incomes from which students can choose; for issue
2, present percentages from which students can choose their position in school. The
participants also made two suggestions: add a question about whether ICT was the first
choice and give an option, when a student chooses ‘no’, to give a reason for ICT being their
second choice; and add a question about the student’s course results before they quit;
Of the seven issues identified, the solutions suggested by the participants for issues 1 and 2
were accepted. Issue 6 was investigated, with the necessary change made to the online
questionnaire to allow only one choice per statement. Issues 3, 4 and 5 were rejected on the
basis that: it was not believed that identifying specific teachers for criticism was necessarily
helpful, and nor was it practical; that there were an adequate number of course questions to
cover a broad range of problems; that the Likert scale had been chosen specifically so that
participants would have to choose a rating, even if that meant choosing neither disagree or
agree. Issue 7 was part of the set up of the online questionnaire and could not be avoided.
Recruiting Participants
While the questionnaire was being designed, a senior academic at each partner university was
identified as a “champion”, who would ensure that requests made for data and for assistance
in contacting students were met in a timely manner. At the same time, the Registrars at the
four partner Universities were sent a letter (see Appendix F) requesting their assistance in:
• identifying students who had transferred from their ICT degree to an unrelated degree
at the university, or who had quit the university altogether, between 2005 to mid
2010;
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• sending out an email or letter to those students requesting their participation in the
survey.
The dual methods of contact were thought most likely to be successful, depending upon the
amount of time that had passed since the student had quit their ICT degree. Students who had
quit during the second half of 2009 or first half of 2010 were most likely to have a viable
student email account while those who quit in previous years might not. These latter students
were contacted by letter using the permanent address recorded by their university. The text to
be used in the letter or email was included in the initial correspondence to the Registrars (see
Appendix F). All four Registrars responded positively and supplied numerical data on:
• the number of students identified;
• the number of each gender; the number of domestic and international students;
• the number enrolled full-time and part-time;
• and the number of school-age and mature-age students in the sample.
However, as each institution has its own system for gathering information about its ex-
students, none provided all the data requested. As a result of the incompleteness of the data
received from the four universities it could not add usefully to that gathered from DEEWR
(2011) (see Appendices A and B).
Letters or emails of invitation were sent to 2,868 ex-ICT students with a response rate of 5
per cent. While the response rate is quite low it should be remembered that surveys are
notorious for their low response rate (Cavana et al., 2001), students living away from their
family home while studying were likely to have left their student accommodation, and the
potential respondents were probably quite negative about their association with their previous
university, given that they had quit their degree.
The data gathered from the questionnaire were analysed in two phases:
I. to identify the most important factors; and
II. to look for trends across various groups.
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3.4.5 The Interviews
Upon completing the questionnaire, respondents were asked to indicate their willingness to
participate in a follow-up interview. The number of respondents who indicated they would
participate in an interview was 53. Of those 53, only 16 responded to emails inviting them to
participate. Six of those willing students participated in semi-structured interviews held at
their University campuses while the remaining ten were interviewed by phone.
In preparing for the face-to-face and telephone interviews each interviewee’s responses were
traced through the downloaded spreadsheets made available by the SurveyMonkey website
(see Appendix G). In this process the demographic data and interviewee’s choices in rating
the statements presented were noted. Of greatest interest were interviewee’s ratings of
strongly agree, agree and neither disagree or agree and these, together with the
interviewee’s written responses to questions about the main reason they had quit their degree,
were used as the starting point for each interview. In accordance with the requirements of the
Human Ethics Committee, each interviewee was asked to sign a form giving their permission
for their interview data to be used for this research (see Appendix H).
The majority of interviews were conducted by phone, rather than face-to-face as originally
planned, but the quality of information provided by interviewees was not compromised as the
same protocol was applied during each interview.
3.4.6 Initial Analysis of Questionnaire and Interview Data
The first round of analysis focussed on the qualitative data, which were compared to the
quantitative data as described in below. Once it was clear that there were no major
inconsistencies between the quantitative and qualitative data, the quantitative data were
analysed twice, once using heuristic analysis and then using statistical analysis. The details of
the first two analyses are presented below.
3.4.6.1 Analysis of the Qualitative Data
The first set of qualitative data was the written responses to open-ended questions provided
by all participants. These were analysed using the Nvivo 9 text analysis program. By this
process it was possible to highlight the most commonly occurring themes and identify those
factors most frequently cited as contributing to attrition.
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The second set of qualitative data was the responses given by participants who agreed to be
interviewed. These interviewees had provided two sets of qualitative data: their open-ended
responses in the questionnaire and their responses to the interview questions. The first set of
data was used to inform the interviews in which each participant was asked to give further
details to further explain their experiences and reasoning for quitting.
3.4.6.2 Analysis of the Quantitative Data
The responses to closed questions were analysed using a heuristic analysis (Kleining & Witt,
2000), as described below, and using statistical analysis. The heuristic analysis aimed to
identify those factors which had the greatest influence on attrition. Given that all the factors
had been ranked on a 5-point Likert scale, two mechanisms were considered. The first is
simply to count the number of respondents who gave each statement a positive rating i.e.
“this factor influenced my decision to quit” which they rated as either 4, agree, or 5, strongly
agree. The second approach was to rank the factors on the basis of their average rating.
The second stage in this heuristic analysis was to set a cut-off point above which factors were
regarded as being of “significant importance”. It must be remembered that one of the goals of
this research is to map potential strategies to the factors leading to attrition. Given that the
literature review identified 140 factors and 10 strategies, a complete mapping of strategies
and factors would produce a very complex set of possibilities, far beyond the resources of any
university to manage. Instead, what is required is a list of the most important factors so that a
sensible number of strategies can be matched to those factors.
Initially cut-off points of 50% or 25% were considered, given that most factors were only
significant for a small group of students, the 50% cut off resulted in too small a list of
contributory factors. Conversely, the 25% cut off resulted in most of the possible factors
becoming contributory factors which would be impractical for any university to address.
Consequently, a 33.3% cut-off produced a viable list of contributory factors because: (1) the
list was small enough for a university to address and (2) the number of students who were
significantly affected by these factors would make it worthwhile for a university to address.
In the remainder of this research, such factors will be described as “contributory factors”.
Highlighting factors that attracted one-third or more of the agree and strongly agree ratings
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combined, resulted in a list that contained a number of factors that had been identified by at
least one third of the participants as having a significant contribution to students’ quitting.
This approach does suffer from two weaknesses. Firstly, as has been stated previously, a
factor which is only rated strongly agree (5 on the Likert scale) by a single respondent may
have overwhelmingly influenced that one individual’s decision to quit. If the student’s
university had addressed that factor, the student would almost certainly not have quit. While
this may occur, the data shows that most students were influenced by more than one factor,
and some by many. Moreover, deploying strategies to address a factor which only influenced
one or two students strongly is not feasible. Thus this “individual breaking point” argument is
rejected.
The second weakness of the heuristic is that it ignores differences between easily identified
groups of students. For example, a factor may have been significant in the decision to quit by
mature age students but not be a significant factor across the whole sample. Groups which
were easily identified and which have been frequently used in previous studies included
females/males, full-time/part-time, traditional/mature age and international/domestic. In
addition, universities routinely gather data about each student’s membership of such groups,
so it would be feasible for universities to tailor strategies to these specific groups if it was
found that some factors applied to these groups more than to the whole population, As
reported in section 4.2.2 some factors were, indeed, far more significant for specific groups.
Consequently the heuristic used to identify contributory factors was extended to include: any
factor rated agree or strongly agree by 33.3% or more of the whole sample, or for any major
demographic sub-group, was deemed to be a contributory factor leading to attrition.
While the focus was on identifying contributory factors, the survey had been structured in
such a way that some statistical analyses were possible and worthwhile. For example, some
survey questions gathered data about factors which, based on the literature, would be
expected to be far more significant for one demographic group rather than another e.g. to
females than to males. Subjecting the data about such factors to statistical analysis would
determine if any such difference did in fact exist. Similarly, some factors might be expected
to have greater relevance to mature age students than to traditional students or to full-time
students than to part-time students and so on.
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As in previous studies, the demographic groupings of interest were dichotomous pairs e.g.
male or female, mature or traditional age. The data gathered about members of these
dichotomous pairs included 5-point Likert responses, so the most appropriate statistical
analysis for the purposes of this study was the independent samples t-test. Independent
samples t-tests were conducted on all the Likert responses for the demographic characteristics
of gender, enrolment load, and age. It was originally intended to conduct t-tests for
international/domestic groups and for postgraduate/undergraduate groups, however the
sample size for the first mentioned group in each of these pairs (i.e. for international students
N=10, and for postgraduate students N=18), are too small for this purpose.
The data from the Likert responses were analysed to determine whether the data for each pair
had equivalent variances and then the appropriate t-test was applied. Given the demographic
data and Likert scale responses, other statistical analysis would have been possible. However
appealing these may be to a quantitative researcher, they would not satisfy the objectives of
the current research, so statistical analyses were limited to those described above.
As part of the larger research project described in section 1.5, surveys were conducted of
members of the Australian Council of Deans of ICT (ACDICT) and the Australian Computer
Society (ACS). These groups are generally representative of Australian ICT academics and
Australian ICT practitioners. The surveys were quite broad-reaching in their scope, but there
were questions posed that were relevant to this research. For example, the members of
ACDICT (46 respondents) were asked specifically to name the strategies that had been used,
or that they could suggest, to attract more female students (see Appendix I). Industry
members (132 respondents) were presented with 5 statements to rate under the heading
“Gender Issues” and asked to write their responses to 3 open-ended questions under the
heading “Perceptions” (see Appendix J) as well as being asked about attraction strategies to
increase female enrolments.
The responses to these survey items were analysed to identify the common themes and
answers given by the respondents and the results were tabulated to enable an in depth
discussion of each significant item to be undertaken. In some instances, this discussion was
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about the absence of responses to particular questions and how those, too, could be
significant.
Although identifying the factors contributing to attrition, and the strategies found in the
literature together with those suggested by the members of ACS and ACDICT, were both
fruitful outcomes, the mapping of those factors to the strategies would provide a means to
understand whether the strategies had successfully targeted the issues experienced by a large
proportion of the ex-ICT students.
This was achieved by analysing each strategy to determine its focus or intended outcome.
Once that had been achieved, it was possible to allocate the strategy to the contributory factor
its focus or outcome would address most satisfactorily. Some strategies were identified as
being able to address more than one contributory factor while each contributory factor could
be addressed, in some cases, by more than one strategy.
3.4.7 Conducting the Capstone Interviews
As the socio-rational approach to understanding attrition has not been undertaken before, it
was believed that it would be reasonable to gather feedback from experienced members of
academia who would be able to assess whether this approach was valid (see section 1.2). To
do this four senior academics were asked to participate in capstone interviews either face-to-
face or via Skype and their responses were subject to a heuristic analysis (Kleining & Witt,
2000).
Each of the four interviewees was asked five questions:
1. What contributory factors come to mind when you think about attrition?
2. Do you think the factors listed in this table are likely to contribute to attrition?
3. Are you surprised by the ordering of the responses?
4. Is there anything you are particularly surprised is not on this list?
5. Do you think it is useful to include both social and rational factors in explaining
attrition?
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Question 1 allowed each interviewee to think about attrition as they had dealt with it and put
them in a frame of mind that prepared them to read through the list of identified contributory
factors.
Having nominated the issues that came to mind, each interviewee was then told that they
would be given a list of the 26 major contributory factors and asked to assess whether they
thought the factors listed were likely to contribute to attrition (Question 2). Each interviewee
was encouraged to comment on the factors as they read through the list and highlight any
factors they questioned.
The interviewee’s comments were noted before they were asked what they thought about the
ordering of the statements in the list (Question 3) and whether they were surprised about the
items that had received the highest positive results. Any comments made about the ordering
were noted.
The interviewees were then asked if there were factors missing from the list (Question 4). If
any factors were suggested, these, too, were noted before a brief explanation of the socio-
rational approach was given by the interviewer.
Once the explanation of the socio-rational approach had been given, the interviewer pointed
out that some of the factors in the list had an ‘(S)’ beside them to indicate which factors were
deemed to fit Tinto’s (1975) Social Integration Model, while those not marked were deemed
to fit Bean’s (1980) Rational Decision Model. Each interviewee was then asked whether they
believed it was a useful approach to include both social and rational factors in explaining
attrition (Question 5) and their responses were noted.
3.4.8 Completion of the Six Research Objectives
Having completed objectives 1 to 5, as described in this Chapter, the final objective is the
provision of guidelines to reduce attrition by mapping suitable strategies to the predominant
factors leading to attrition. This was done by tabulating the contributory factors identified by
the ex-ICT students as leading them to quit, determining the outcome or focus of each
strategy and identifying the best fit between each strategy and factor, based upon the socio
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(S) and rational (R) categorisations already determined for each one. The objective also calls
for recommendations to be made and these are detailed at the end of Chapter 5.
3.5 Ethical Considerations
Conducting research on human beings (or animals) requires that careful thought be given to
any negative consequences for the participants, including the researcher. For the subjects of
the research it is important to consider the impact of the questions on them and to ensure the
wording is clear and unambiguous (Lee & Lings, 2008, p282). In the case of this study, the
subjects were not asked to identify themselves in ways that were outside the typical
demographic questions asked regularly of people. While it was possible that some
respondents might find that the questionnaire brought to mind a potentially painful life
decision, it was decided that the chances were low and any distress would be short-lived.
Nonetheless, a number of mechanisms were used to make the respondents as comfortable
with this process as possible. Participants were informed by email or letter, and by the
welcome page at SurveyMonkey, (see Appendix E) that they would remain anonymous and
be free to opt in or out of completing the survey at any time prior to submission.
Only those respondents who chose to identify themselves by completing the required fields at
the end of the survey were asked to participate in interviews which were conducted at the
respondent’s university or by telephone. The PhD researcher ensured that her location, who
she was meeting, where they were meeting and when the meeting should end, were all known
to a third party (Saunders, Lewis & Thornhill, 2007, p190) as a means of guaranteeing her
personal safety.
The research undertaken in this study was conducted after receiving permission from the
Human Research Ethics Committee (Numbers HE10/126, HE10/198, HE11/093) of the
University of Wollongong, granted on 15/4/2010, 30/07/2010 and 17/3/2011 respectively (see
Appendix K).
3.6 Conclusion
This chapter presented the justification for the research paradigm and data collection
methodology and discussed the research procedures. Each of the six objectives of this
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research was detailed along with an explanation of how each would be completed. The
methods used in the research were discussed in some depth and this was followed by a
heuristic analysis of the Capstone Interviews conducted with four senior academics. The
chapter concluded by indicating how the six research objectives had been completed and this
discussion was followed by consideration of the ethics of conducting surveys and interviews
and their potential impact on the participants. The following chapter will present the results
of the research.
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4 DATA ANALYSIS
4.1 Introduction
The previous chapter explained the research paradigm, and the methods applied to achieve
each of the six objectives of the research were discussed. The chapter concluded with an
acknowledgement of the ethical considerations inherent in research on human subjects. This
chapter describes the sources of data gathered, and presents the results of the analyses used to
identify the contributory factors leading to attrition from ICT degrees. This data will be
presented in three sections and is derived from information supplied:
• by ex-ICT students;
• from the literature;
• by members of the Australian Council of Deans of ICT (ACDICT); and
• by individuals who are working in the ICT industry and are either: members of the
Australian Computer Society (ACS) or university Advisory Board members.
The main sections in this chapter will focus on each of these data sources in turn, although
some sub-sections will present both quantitative and qualitative data to better understand the
factors contributing to attrition from ICT degrees. All tables referred to in this chapter are in
Appendix L. The naming convention adopted for the tables in that Appendix is L1, L2 etc.
The literature review identified many factors which researchers across the western world
have argued play some part in attrition from higher education. All of these were contributory
factors in some individuals’ decision to quit. However, the results of this study have shown
that a number of the identified factors did not, in fact, contribute to a significant proportion of
those students’ decisions to quit. As a result, it is necessary to make a clear distinction
between the contributory factors and the non-contributory factors. In this research, for
example, the survey of ex-ICT students found that 4.2% of respondents agreed or strongly
agreed that living in student accommodation was a factor in their decision to quit. However,
this was not a factor for 95.8% of respondents, so it is not a factor that influenced many
students’ decision to quit. From a pragmatic point of view, then, this is not worthy of further
investigation. Conversely, the survey found that 43.7% of ex-ICT students agreed or strongly
agreed that having picked the wrong degree was a factor in their decision to quit. Given the
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proportion of students affected, this must be considered a significant contributory factor. So,
for the purposes of this research a “contributing factor” is any factor which more than one
third (33.3%) of students agreed or strongly agreed was a factor in their decision to quit.
Factors which have less than 33.3% agree or strongly agree responses will be considered to
be non-contributory factors.
4.2 Student Data
This section is divided into 3 sub-sections corresponding to the predominant types of data
used in each sub-section. The first section will present the descriptive analysis which is
further divided into: demographic data; course data; participant data; and reason for quitting.
The second section contains statistical analysis and the third section contains the differences
in factors contributing to quitting between types of students, for example: gender; study load;
age; residential status; and enrolment level. It is followed by a discussion of the responses
provided to the open-ended questions in the survey and analysis of the data gathered from
interviews and concludes with an examination of contributory factors identified by significant
groups within the student population.
4.2.1 Quantitative Survey Data
This section presents a descriptive analysis of the data on: demographics; the course;
participation; and main reason for quitting is carried out to identify trends.
4.2.1.1 Demographic Data
The information of a demographic nature gathered from participants included: gender; age
when enrolled; final ranking at high school; ethnicity; language spoken at home;
parent’s/guardian’s gross income; hours worked per week; and marital status and dependents.
A total of 154 ex-ICT students completed the survey. Not all respondents answered all
questions, so the actual number of respondents will be indicated for each set of results. Of the
total respondents18.8% were females (N=29) and 81.2% were males (N=125). The relatively
small number of women is consistent with the numbers of females studying ICT at the
universities involved and with the literature – as demonstrated in Chapter 2 – on the notably
low female participation in ICT education at the tertiary level in western industrialised
countries (Cory et al., 2006; Craig et al., 2007; Lewis et al., 2007; Ogan, Robinson, Ahuja &
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Herring, 2006; Siann & Callaghan, 2001). Based on this sample, there is no evidence that
females quit more often than males.
Of the total respondents, 47.4% (N=73, female=18, male=55) were school leavers, up to 19
years old and 52.6% (N=81, female=11, male=70) were mature age, 20 years or older. This
distribution reflects the fact that universities are no longer predominantly populated by recent
high school students (Bradley et al., 2008). Based on this sample there is no evidence that
younger students are more likely to quit than mature age students.
High school ranking is often seen as an indicator of potential university performance and,
therefore, an indicator of the likelihood to persist at university (Bean, 1980; Bean & Metzner,
1985; Braxton, Sullivan & Johnson, 1997; Hillman, 2005; McInnis et al., 2000b; McKenzie
& Schweitzer, 2001; Spady, 1971; Tinto, 1975). Since 81.8% of the female (N=21) and
63.2% of the male (n=67) survey participants ranked themselves in the top 30% of their high
school cohort, this sample strongly refutes this claim (Table L1). A significant majority of the
respondents who quit indicated that they were in the top 30% at school and even from the top
10%.
Self ranking of their place at high school was the replacement for a question asking
respondents to supply their ENTER score which, in the pilot study (see section 3.4.4), was
criticised by the international students for not applying to them. Additionally, it was
recognised that it would also not necessarily apply to mature age students.
Respondents were also asked to indicate their ranking at university before they quit as a
means to gauge whether students quit because they are struggling with their academic work.
Just over one third (38.3%, N=49) of students who quit were in the top 30% of their class,
while only 28.9% (N=39) were in the bottom 30% (Table L2). More than one third of male
respondents (38.6%) chose rankings which indicated they had performed well before quitting
their studies, while only 26.9% of the females chose those rankings.
As both the final ranking at high school and final ranking in the ICT degree before quitting
are not objective measures, but the result of self-ranking by respondents, it must be
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acknowledged that there could be some bias introduced by the ex-ICT students who provided
responses.
To understand the socio-economic status of students who quit their studies, respondents were
asked to choose one of three categories to give a general idea of their parent’s/guardian’s
combined gross income. Given that many of the respondents were mature-age students; this
question was not pertinent to some respondents (Table L3).
According to the Australian Bureau of Statistics (ABS, 2013) the median gross household
income in 2011 was approximately $64,000 so 23% of the students who quit were low-SES
students. These students will typically have been to poorer schools, had fewer resources at
home, will not have had access to tutors and so could be expected to be disadvantaged
compared to the students in the top income category (40%). In recent years the Australian
government has recognised that the needs of low-SES students have not been met by
universities. This has resulted in the government setting national targets for student
attainment and establishing the Tertiary Education Quality and Standards Agency (TEQSA)
to enhance the quality of higher education for all students (James et al., 2010, pv).
As some students may be supported by their parents during their studies, while others would,
of necessity, need to support themselves – and to gauge how much of a student’s time was
occupied with this activity – participants were also asked to indicate how many hours they
had worked per week while they were studying. 15 students (11%) indicated that they had not
worked at all while 11 students (8%) had worked up to 5 hours per week, 55 (40.4%) had
worked between 5 and 20 hours per week. Toward the top of that range, the paid work being
done could be expected to have a negative impact on the student’s ability to study (James et
al., 2010, p54). Even more alarming 34.6% (N=47) had worked from 20 to 40 hours per
week and 11.8% (N=16) worked over 40 hours each week. As McKenzie and Schweitzer
(2001) pointed out, full-time students working more than 15 hours per week are more likely
to quit as it may “negatively affect persistence” (Kuh, Kinzie, Bridges & Hayek, 2007, p28)
and this is supported by findings over several decades (Krause et al., 2005; McInnis &
Hartley, 2002; Price et al., 1992; Scott, 2005; West et al., 1986). Clearly those working 30 or
40 hours per week are almost guaranteed to find that holding down the equivalent of a full-
time job and full-time study load is too much to maintain (Table L4).
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This tendency to work during the semester has been noted in the First Year Experience
surveys which have been conducted in Australia every 5 years since 1994 (James et al., 2010;
Krause et al., 2005; McInnes et al., 1995; McInnis et al., 2000b). In the most recent
publication, James et al. (2010) note that 61 percent of students worked during the semester
compared with 55 per cent in 2004 and 47 per cent in 1994 (Krause et al., 2005).
Most of the respondents in this study indicated they were single when they enrolled at
university (Table L5). Of those with dependent children, only one female and one male were
single. The proportion of both males and females who were married but had no children is
almost identical suggesting that being married does not affect females more than males.
Having said that, the proportion of married females with children who quit is nearly double
that of married males with children.
Although respondents were given 14 choices of ethnicity from which to choose, the majority
(104) of the respondents chose ‘Australian’ or ‘British’ and they also indicated that the
language spoken at home was English. The remainder (32), nominated various ethnicities
(Table L6). Of the 32 who nominated various ethnicities, 16 indicated that they spoke English
at home; leaving 16 students who spoke a language other than English at home (Table L7).
4.2.1.2 Course Data
The first three sets of course data (Table L8) are: enrolment load (full-time or part-time),
residential status (domestic or international), and enrolment level (undergraduate or
postgraduate).
Of the total respondents, 74% (N=113) of the participants had studied full-time. 92.6%
(N=126) were domestic students while 86.6% (N=116) were undergraduates. The low
proportion of international students (7.4%, N=10) reflects the difficulty in maintaining
contact details for those students once they leave the country. This is also the case for
postgraduates as a large proportion of them are also international students.
The next set of course data is about the degree that the student was enrolled in when they
decided to quit. It is notable that these proportions are roughly in line with those of
respondents to an ACS survey conducted in 2012 (ACS, 2012, p32). 42.9% (N=58) of the
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respondents indicated that they had chosen to enrol in IT, while 34.1% (N=46) had enrolled
in Computer Science, 13.3% (N=18) in Information Systems with the remainder (N=13) were
divided between CSE (N=2), EE (N=4), SE (N=4) and TE (N=3) (Table L9).
Despite there being numerous findings (see section 2.2.1) which support the assertion that the
majority of quitters leave their degree in their first year and early in that year, respondents did
not consistently indicate that was the case for them (Table L10).
Although the majority of female students (76.2%, N=16) had quit in the first half of their first
year, the male students were almost evenly split between those who quit in the first half of the
year (52%, N=52) and those who quit in the second half of their first year (48%, N=48).
The last set of course data is about whether the ICT degree in which students had enrolled
was their first choice. This was not only of interest in previous research (Price et al., 1992;
Rhodes & Nevill, 2004) but was also suggested by students who participated in the pilot
study of this questionnaire (see section 3.4.4). It is reasonable to suggest that students who
have enrolled in a degree that was not their first preference may be underprepared for the
rigours of the course and may be less committed to completing it. The figures appear to bear
this out for female quitters (Table L11), as more than one-third (38.5% N=10) were enrolled
in a degree that was not their first choice. In stark contrast, less than one-tenth of male
quitters (8.5% N=8) had enrolled in a degree that was not their first preference.
4.2.1.3 Integration and Participation in Student Life Data
As much has been made of the need for new students to integrate into the unfamiliar
environment of university life, and become accustomed to the autonomy and freedom that is
inherent in being a young adult, universities have devoted their attention to this aspect of the
university experience for first year students. It has been common practice for some years for
universities to organise events that might be named “Orientation” or “Open” days for
students. Some schools or faculties within each university have also planned functions that
coincide with those university-wide events or had organised gatherings during the weeks
following the start of the university year. As these are viewed as important aspects of settling
into university life, those who miss, or do not make the most of, these opportunities may not
feel that they are part of their university or school (Trotter & Roberts, 2006). Although 72%
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(N=107) respondents had attended Orientation at their university only 32.8% (N=46) had
attended functions organised by their faculty or school (Table L12).
Of those who gave a reason for not attending their school or faculty functions, almost half
(45.3%) stated that either none had been organised or that they had not been made aware that
functions had been held, while a further 32.1% were frequently quite blunt in indicating their
disinterest:
“I am really not into all the social lives of university. I go in, attend the lectures, do
the assignments and get out.” Male, 20, CS;
“Only functions were trade shows which did not interest me.” Male, 18, IT;
“None were appealing, especially given the people I met through my classes. Also,
not enough girls.” Male, under 18, GamesTech.
The literature (Bennett, 2003; Trotter & Roberts, 2006) suggests that students may feel
isolated and lonely if they miss the start of their course. Only 4 of the 132 respondents had
started their course after it had begun and three gave the following reasons:
“enrolment mix up between [college X] and [university Y].” Male, 24, IT;
“Advanced standing.” Male, 26-35, EE;
“I was a little disorganized.” Male, 46-55, Math
So, the majority of respondents had attended Orientation run by their university and had
started their course on time. Those who did not attend school or faculty functions believed
none had been planned by their school or had not heard about functions being held, or were
uninterested in attending such events.
4.2.1.4 Main Reason for Quitting
As discussed in section 3.4.4, surveys are notorious for their low level of initial response
(Krause et al., 2005) and for participants dropping out before finishing (Lee & Lings, 2008),
thus the last question on the first page of the questionnaire was intended to capture at least the
main reason for quitting in case respondents did not finish the questionnaire. Respondents
were asked to indicate whether they had quit because of personal reasons, something about
the course, or a combination of personal reasons and the course. All of those who began the
survey answered this question, with 25 (86.2%) of the females and 58 (46.4%) of the males
choosing the third option (Table L13).
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This result is significant in three ways. First, for most students, the decision to quit is based
on multiple factors. Over half the respondents (53.2%) indicated that their main reasons for
quitting included both a personal and course reason. So, even when asked to consider the
most significant reason, they responded that a combination of a personal reason and a course
reason led to their decision to quit. Second, the personal reasons are more likely to be social
reasons while issues with the course may be more rational decisions. Third, many people
believe that the main reason for quitting is only the course. However, fewer than 20% of
respondents who quit cite only the course as the main reason though, for some, it was a
combination of course factors not just one. In fact, 27.9% of respondents’ main reason for
quitting was a personal factor quite unrelated to their course.
In order to assist participants to think about various aspects of their life at the time they quit,
the Likert scale statements presented in the questionnaire were divided into three Survey
Sections. Survey Section 1 was titled ‘University Experience’ and presented statements of a
general nature which could not be assigned to either Survey Section 2 ‘Course Experience’ or
Survey Section 3 ‘Life Experience’. Following a brief description of the responses to the
Likert scale statements, each statement will be discussed in the order in which they were
presented to the participants.
The first aspect to note about the set of responses is that they are predominantly negative, i.e.
strongly disagree (SD) or disagree (D). For example, when asked if “staff being unfriendly”
influenced their decision to quit, 61% of the participants responded negatively (SD=25.2%,
D=35.8%). The fact that the majority of responses were negative was expected because it was
thought that most students would only be strongly influenced to quit by a small sub-set of
factors. For most students most factors would have little influence and thus would receive a
negative (SD or D) response. Where that positive response reached 33.3% or higher by the
participants, it has been highlighted in bold typeface (see Appendix L for relevant Tables).
4.2.1.5 University Experience
The most frequent positive response (A or SA) given by 50.5% (N=77) to the University
Experience (Survey Section 1) was that there were too many distractions preventing them
from concentrating on their studies (Table L14).
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Universities use social activities of various kinds: sports; special interest clubs; drinking and
eating establishments; fairs; and themed days or weeks - to name but a few - to create a sense
of inclusivity, promote diversity, and project the image that higher education can be a social
as well as educational experience. Although the statement did not name these activities, ex-
students could have interpreted them as ‘distractions’ and credited one or a number of these
for contributing to their inability to put the time and effort needed into their studies. Of
course, fellow students, lecturers, general staff or tutors could also have provided distractions
as could external relationships with friends, partners and families. Other positively rated
factors included the challenge of organising a timetable with no clashes (27.6% positive) and
getting help when needed (26.7% positive) though neither are contributory factors for the
purposes of this study.
One of the goals of this research is to identify a set of factors which have influenced a
significant proportion of the participants to quit. These were defined as “contributory factors”
that influenced more than 33.3% of all respondents or of any specially targeted sub group,
e.g. females, part-time students, low socio-economic students, etc. Although it is a concern
that 17.2% of respondents agreed that university staff were not friendly, it cannot be treated
as a contributory factor in this instance. The same can be said of the statement about
‘facilities’ which was left purposely vague to allow for a variety of interpretations. It appears
that, whatever the interpretation, the respondents did not believe the facilities or lack of them
contributed to their decision to quit as only 14.4% indicated that this caused concern. As
stated earlier, the “too many distractions ...” statement in this Survey Section is the only
contributory factor as the A rating (32.7%) and SA rating (17.8%) make the responses from
participants strongly positive at 50.5%.
4.2.1.6 Course Experience
This section is divided into four sub-sections: Teaching; Course; Teaching and Learning
Environment; and Preparedness and Other Student Issues, each of which is described and
shown in Table L15.
Teaching
The most frequent positive response to factors related to teaching was that classes were
boring (42.4% agreement). Boredom has been identified as one of the most frequently cited
causes of truancy in students (Fallis & Opotow, 2003; Mann & Robinson, 2009). Although
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Mann and Robinson (2009) were aware that boredom had been studied in the school system,
they recognised that it had not been investigated as a specific issue at university. Their study
found that individuals reacted differently in the same circumstance which, in their research,
was attendance at a lecture. Their findings correlate with those of Farmer and Sundberg
(1986) who had devised a scale for Boredom Proneness to measure the likelihood of a person
experiencing boredom. Mann and Robinson (2009) cite a number of studies that found
leaving school either temporarily or permanently could be attributed in large part to boredom,
which is confirmed by the results of this current study. Although boredom proneness has
been identified as a personality trait (Farmer & Sundberg, 1986), teaching methods that do
not engage the learner have also been cited as contributors to students’ experience of
boredom (Fallis & Opotow, 2003). The traditional instructional design inherent in the linear
techniques employed at university is critiqued by Liaw (2004) when describing Cognitive
Flexibility Theory. This theory states that “for a learner to fully comprehend the complexity
and erratic variability of information, it must be accessible to the learner in a manner that
more closely mimics the nonlinear nature of the domain” (Liaw, 2004, p315) which is in
opposition to the traditional lecture, textbook and tutorial methods used in university teaching
and may contribute to the experience of boredom in the lecture theatre (Mann & Robinson,
2009).
Many participants also found the pace of teaching too fast (32.2% agreement). As one of the
participants put it:
“It was uninteresting and not exciting. I felt like I was just memorising information,
not using critical thinking and not *really* learning” Male, 19, IT
The majority of ex-ICT students did not believe their teachers were unprepared (only 9.8%
positive) or that their knowledge was out of date (15.7% positive) so it appears that, for the
majority of former students, these were not factors contributing to them quitting their degree.
In fact, the only contributory factor is that “The classes were boring” with 25.8% of the
respondents rating this as agree and 16.6% rating it as strongly agree with a combined
positive rating of 42.4%.
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Course Content
Several statements about course content received strongly positive responses (A or SA)
about the balance between application and theory such as: lack of workplace focus (37.1%
positive), lack of practical applications (31.2% positive), lack of a business focus (30.9%
positive) and too theoretical (29.0% positive). However, the responses to the last three
statements fell below the threshold to be considered contributory factors (Table L16).
The identification of ICT courses as ones that are not workplace focussed (the only
significant contributory factor in this group) suggests students are considering their future
employment or are already engaged in workplaces, either in a placement to gain experience
as part of their course or as employees enrolled in a university course to supplement or
increase their knowledge and qualifications. In either scenario the critique of university ICT
courses as lacking input from or insight into business and the workplace from ex-students in
four states is a clear indication that this is a problem. This quote from a participant reflects a
common sentiment among students:
“I lost interest in IT through the university's conception of what IT is. It was
presented as highly technical, highly mathematical and very individualized. In
reality, IT has close links with business, work in teams and programming is a small
portion of what IT is about.” Male, 17, IT
Teaching and Learning Environment
Issues associated with the teaching and learning environment were also considered
important: some respondents felt that they did not belong (36.1%) in the teaching
environment, that it did not suit their learning style (35.5%). The other statements such as:
the teaching environment was not welcoming (25.7%); I was in the minority in my classes
(25.3%); The focus was on individual activities rather than groups (19.4%); and The course
was too competitive (13.2%) fell below the threshold to be considered contributory factors
(Table L17).
The two contributory factors in Table K17 are both related to student integration. The
strongly positive rating of “I didn’t feel I fitted in or belonged” indicates that students had
not integrated into the social life of their university and “Academic environment did not suit
my learning style” shows that students had not integrated into the academic life of their
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university. Tinto’s (1975) Social Integration Model posited that both types of integration
were vital to the success of students while Bean’s (1980) Rational Decision Model also had
a measure of integration. Both Tinto (2012) and Bean (Bean & Eaton, 2001) continue to see
integration as a valuable means by which to understand students’ failure to persist.
Preparedness and Other Student Issues
In this part of the questionnaire the three contributory factors identified by participants were
the course not meeting their expectations (53.4% positive), not enjoying classes (49.3%
positive) and not understanding the concepts (33.5% positive) (Table L18).
Although universities believe they provide a wealth of information to prospective students
while they are in the process of choosing a degree, studies in western industrialised countries
do identify the mismatch between the student’s expectations and the reality of the course
(Benjamin & Hollings, 1995; Christie et al., 2004; Clark & Ramsay, 1990; Connolly &
Murphy, 2005; Craig, Fisher, Forgasz & Lang, 2011; Ozga & Sukhnandan, 1998; Yorke,
1998).
As a result of any one or a combination of these, the student attends their first lectures and
tutorials only to find that they have misunderstood or been misguided by the information
available to them. This may be one explanation for students not enjoying their classes. It
may not be the content of the instructional material that results in classes not being
enjoyable, of course. It may be that many of the other factors discussed here will have
contributed to the sense that the course and the classes are not enjoyable.
Some students felt that they did not understand the concepts (33.5% positive), or terms used
in the course (22.8% positive) and believed that they did not have the expected background
knowledge (26.9% positive). This perception is illustrated by the quote:
I didn't have the expected background knowledge; the courses were definitely geared
towards those with more pre-existing knowledge. Female, 18, IT
Although only the first of these three statements can be considered a contributory factor, it is
reasonable to argue that not understanding the terms and not having the expected background
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knowledge are related and may contribute to students’ results not being as high as expected
(29.5%).
The social aspect of study also received attention with a number of participants (26.9%)
agreeing or strongly agreeing that they didn’t make friends with classmates. Very few
students believed that it was unacceptable to be smart (4.8%) and the ranking of these
statements is below the cut off point so they cannot be considered contributory factors.
4.2.1.7 Life Experience
In Survey Section 3 about students’ Life Experience (Table L19) many participants indicated
that they felt that they had picked the wrong degree (43.7% positive). This sentiment implies
a lack of interest and engagement with the degree content. Ozga and Sukhnandan (1998)
provided one explanation for this when they argued that the need to attract students has the
potential to result in poor or wrong choices when the marketing materials present courses
inaccurately or students are unable to access information that would allow them to make an
appropriate choice.
Financial pressures may be of concern to students with Bennett (2003) and Cabrera et al.
(1993) finding that it was a major predictor of attrition. This is supported by the finding that
the second highest rated factor in Table 4.19 was “Attending university was too expensive”
(agree 21.1%, strongly agree 16.6% = 37.7% positive rating). Since mature age students with
a full-time job are not often able to defer their fees through the HELP scheme (Higher
Education Loan Program (Australian Government, 2012)), it might be expected that some
mature age students would agree or strongly agree with this statement, and this is supported
by the positive rating made by 34 of the 80 mature age students (42.5%). However, 21 of the
73 traditional age students (28.8%) also gave this statement a positive rating.
Conflicts with work commitments were also a common issue; 36.4% agreed or strongly
agreed that they experienced conflict with work commitments, and 33.4% noted that their
study timetable did not fit with their work commitments. The division between respondents
who did not work (N=15) or had worked up to 20 hours per week (N=65) and those working
more than 20 hours per week (N=71) reflects the mixed response about whether they
experienced a conflict between work and attending classes as some students working a small
number of hours may have been employed on campus or by employers willing to be flexible.
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Factors such as these make it difficult for students to fully engage with their studies and are
likely to work in combination with other issues to precipitate attrition as illustrated by the
following quote:
“Finances were a big issue; Public transport from the southern highlands was almost
nonexisant (sic), thus I had to drive - petrol was costing me greatly. To make the
money to get to uni, I had to spend all my 'spare' time working, which of course meant
I had no time for uni. Stress of both money and failing classes compounded, making
both problems even worse.” Male, 18, IT
This is supported by other researchers who have found that there may be “two or three
reasons for leaving” (Hermanowicz, 2006, p25) or that “several bundles of influences must
be taken into account” (Georg, 2009, p649-650).
Of the eighteen factors in this section, only these four can be considered contributory factors
as the other statements received rankings below the threshold of 33.3%.
4.2.1.8 Conclusion
The results above demonstrate the range of issues that can contribute to student attrition. For
the purposes of this research, a number of them have been rejected as contributory factors
because they did not attract more than one third of the respondents’ agreement. Table L20
presents the twelve factors which received a combined ranking of agree (A) or strongly agree
(SA) by more than 33.3% of the respondents. This table also indicates the section of the
questionnaire from which the statements were taken and classifies them as social (S), rational
(R) or a combination of social and rational (S&R):
This is the first indication that both social and rational factors combine in a student’s decision
to quit. As discussed in section 2.5, other researchers have attempted to use the models of
Tinto (1975) and Bean (1980) to demonstrate this before but did not succeed in their
endeavours. It is also apparent that individual students rarely quit for just one reason. An
individual may have had a variety of lowly-ranked experiences which, for them, combined to
make their persistence at university impossible. Personal issues, university issues and course
related issues could combine to put pressure on students who may respond by quitting their
studies. In some cases, ex-ICT students feel they have made the decision willingly, but in
others they are very conscious of the lack of support received, as illustrated by these quotes:
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“I found the attitude of the faculties, the structure of courses and resistance to
reasonable student requests very deflating and negative” Male, 21, Comp Eng
“Was not provided with enough information about how I should have acted when I
got very sick, and even though I handed in withdrawal forms, was treated unfairly
when it took staff 3 months after constant hassling by me to get information, and in
result still getting charged and not withdrawn from my subjects”. Male, 18, IS
It seems clear that the decision to choose 33.3% as the cut off point for identifying
significant contributory factors is reasonable. This has returned the twelve most significant
factors which is probably a manageable set of factors for a university to try to control or
improve. Since the questionnaire is the compilation of reasons for attrition found in the
literature, it is clear that some of the ex-ICT students were influenced by the factors
identified in that review. Interestingly, several of the factors identified by the ex-ICT
students as contributing to their decision to quit are not given the emphasis in the literature
that they apparently deserve. The shift away from full-time study as a student’s sole focus,
and dependence upon a job to survive – whether the student is enrolled with a full-time or
part-time study load – has only recently been recognised. The teaching of ICT has also been
viewed as unproblematic and the assumptions that inform that practice, and affect the
teaching environment, have not previously been revealed and, more importantly, have only
just started to be questioned (Roberts et al., 2011a; Roberts et al., 2012).
4.2.2 Differences between Types of Students
This section reports a statistical analysis of the survey data to identify any statistically
significant differences between types of students.
As discussed in some detail in section 2.2.2, various student characteristics have been
identified as potential contributing factors to attrition; these include gender, enrolment load
and age (Barker et al., 2009; Long et al., 2006). Residential status (i.e. whether the student
was and international or a domestic student) and enrolment level (i.e. whether the student was
enrolled as an undergraduate or postgraduate) were also considered possible factors for
attrition. However, given the small numbers of international students (N=10) and
postgraduate students (N=18), the samples sizes for these two groups are too small to conduct
t-tests.
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Independent samples t-tests were used to determine whether the first three factors had a
significant influence on students’ reasons for quitting their ICT course (note: a significance
level of p < 0.05 was used). Tables L21 to L24 provide the mean agreement ratings for each
subgroup for those factors where there was a significant difference.
Although every ex-ICT student who participated in the survey provided answers to the first
page of six questions, the survey was fully completed by 139 ex-students resulting in an
89.1% completion rate. Of the total survey participants who answered the demographic
questions: 29 were female and 125 were male; most of the ex-students were enrolled full-time
(N=113) with 40 studying part-time; and the split was reasonably even between traditional
age (N=73) and mature-age (N=80) respondents; while the majority were domestic (N=126)
students, 10 indicating they were international (all of them male).
4.2.2.1 Gender Differences
Australian government statistics on enrolments in ICT degrees show that of all commencing
students the percentage of women, on average, was 21% between 2001 and 208
(approximately 24,000 students have enrolled each year and approximately 5,300 of those
were female, on average). The percentage of women enrolled in an ICT degree identified in
the participating universities’ data (MU 15%, SUT 13%, UOW 12.5%, UQ 24%), as well as
the proportion of those participating in the survey (18.8% female), was lower than those in
the government statistics and this serves to support the findings made over several decades by
overseas researchers on the notably low female participation in ICT education at a tertiary
level (see section 2.3.2).
Several possible reasons for attrition relating specifically to gender issues were included in
the survey. The levels of agreement of the female participants are reported in Table K21 and
those of the male participants in Table L22.
Gender issues did not appear to be relatively more important to the female respondents.
Whilst the lack of females was certainly noted (48.1% agree, 14.8% strongly agree = 62.9%
positive rating), and over a quarter of the female students agreed that the course was male
oriented (20.7% agree, 6.9% strongly agree = 27.6% positive rating) this, and the ranking of
other statements in this section were below the cut off to be considered contributory factors.
Few female students felt that male staff did not encourage them to participate (18.5%
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positive rating) and only one female (3.7% of the 27 who rated the statement) agreed that
male students did not let them participate. Two females acknowledged, by choosing agree or
strongly agree, that male students or staff spoke in a sexist manner. The levels of agreement
with these issues were, however, not significantly different from those of the male ex-
students (t=1.27, p=0.206; t=1.71, p=0.089 respectively).
Gender was, however, found to have a statistically significant influence on students’
agreement with several of the other possible reasons for leaving their ICT course (Table
L23). Males were statistically significantly more likely to believe that there were too many
distractions preventing them from concentrating on their studies (t=-2.34, p=0.021). Females,
on the other hand, were more likely to believe that they didn't understand the concepts (t=-
3.82, p<0.001), didn't have the expected background knowledge for the course (t=-2.25,
p<0.026), or didn't understand the meaning of terms used in the course (t=-2.30, p=0.008).
Previous research has suggested that female students have no less ability to undertake ICT
courses than male students (Beyer et al., 2003), however, it has been found that female ICT
students lack confidence in their ability to achieve their educational goals (Beyer et al., 2003;
Cohoon, 2007). The findings of this study are consistent with this previous research. Lack of
confidence in ability to undertake study in a discipline that is perceived to be challenging is
thought to contribute to low enrolment rates of females (Gras-Velazquez et al., 2009; Manis
et al., 1989). It also appears to contribute to attrition, preventing female students from
accessing the benefits that can flow from an ICT career. Actions that increase confidence
should be pursued and these might include mentoring (Cohoon, 2001) and early exposure to
work integrated learning.
A number of females indicated that their results were not as high as they had expected (t=-
2.40, p=0.018), that they were in the minority (t=-2.34, p=0.021) and that they felt they had
picked the wrong degree (t=-2.04, p=0.043). Previous research has shown that female
students who quit ICT degrees tend to have higher grades than male students who do not quit
(Strenta, Elliott, Adair, Matier & Scott, 1994), yet they are more sensitive to perceptions that
their grades are lower than those they received in high school (Jagacinski et al., 1988). Being
in the minority can add to this sensitivity because it sends a clear message to the minority that
they are ‘unusual’ or ‘different’ and that they ‘stand out from the crowd’. This probably isn’t
intended, but the result is the same as the minority members of the class feel less than
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welcome. In combination with other concerns, this can lead them to believe that they have
picked the wrong degree.
Differential attrition of female students in this way is a major loss to the ICT profession, but
it is not purely a gender issue, as Strenta et al. (1994) found in other disciplines, such as
science and engineering, where students had the same grades their persistence was the same,
despite their gender.
Unexpectedly, there were no statistically significant differences in response to most of the life
issues: female students were not more likely to be affected by issues such as pregnancy or
dealing with family illness.
4.2.2.2 Study Load
The majority of previous research has focussed on students who were studying full-time
(Braxton et al., 2000; Christie et al., 2004; Crisp et al., 2009; Harrison, 2006; Price et al.,
1992; Stratton et al., 2008). However, many students study part-time in order to be able meet
their work or family commitments, and previous research has shown that part-time students
are more likely to quit their studies (Bean & Metzner, 1985; Long et al., 2006). It might be
expected that part-time students face greater pressures, so differences in their reasons for
ceasing to study are of interest.
Participants who had been full-time students differed statistically significantly from those
who had been part-time in their levels of agreement with many of the reasons for attrition
(Table L24). In all except two cases, students who had been full-time had stronger levels of
agreement. This included all differences relating to perceptions of the university environment
and the course and how it was taught. The only two factors which were statistically
significantly different for part-time study were associated with the lives of the students.
For example, full-time students were statistically significantly more likely to believe:
• that they had picked the wrong degree (t=4.050, p<0.001);
• that classes were boring (t=3.41, p=0.001);
• that they did not have sufficient background (t=2.19, p=0.030);
• or understand the concepts (t=3.17, p=0.002).
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Traditionally part-time students have been perceived as facing significant pressures
associated with juggling the competing demands of work, family and study (Long et al.,
2006). Conversely, full-time students have been viewed as having more freedom to devote
their time and attention to their studies (Krause et al., 2005). Analysis of the hours worked by
the participants who were full-time students before they withdrew showed that 59.4% were
working over 10 hours per week, and 27.7% were working over 20 hours per week. This
suggests that in some respects full-time students may be under greater pressure than part-time
students, and that this has led to an increased sensitivity to a range of issues that affect their
satisfaction with their studies and so predispose them to attrition.
The two issues with which students who had been part-time were more likely to agree related
to conflicts between their studies and their work commitments. This is consistent with Long
et al.’s (2006) findings which are reflected by the following quote:
“Financial struggle. I was unable to support my family while attending University Full
Time. I tried going part time but this was still too hard. I tried external, however working
full time and then trying to study all became too stressful” Male, 25, CS
4.2.2.3 Age Differences
Although there is some evidence that older students are more likely to quit their study
(DEST, 2004), the factors that lead older students to quit are unknown. Table L25, Age
Difference Experience, lists the factors leading to attrition where there were statistically
significant differences in agreement between younger participants (20 or younger when they
enrolled) and older students (21 or older when they enrolled).
The findings here showed that it was the younger group who had statistically significantly
higher levels of agreement, for all but five of the proposed factors. This included all
differences relating to University Experience and Course Experience and all but one of the
Life Experience reasons.
The higher levels of agreement by younger students were on the course related issues such as
not enjoying attending classes (t=6.42, p=<0.000), the course not meeting their expectations
(t=-2.71, p=0.010), classes being boring (t=5.14, p=<0.001), and not understanding the
concepts being used (t=4.36, p=<0.001). These higher ratings suggest perhaps that younger
students had made a hastier or less-informed choice when starting their ICT course. It is,
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however, necessary to acknowledge that even with the best information and advice; it is still
possible to fail to convey the rigours of a course.
The five reasons with which the older students showed higher levels of agreement than the
traditional age cohort were that university was too expensive (t=-2.19 p=0.030), there was a
conflict between study and work (t=-2.75, p=0.007), they had experienced a death or serious
illness in the family (t=-2.59, p=0.010), lost their job (t=-2.01, p=0.047) or they or their
partner had become pregnant (t=-2.87, p=0.005).
Illness or death of a family member, the loss of a job and pregnancy are serious life events
that are often linked to stage of life, so the differences are what might be expected. The older
students also agreed more that they experienced conflict between study and work
commitments (t=-2.37, p=0.007) and that university was too expensive (t=-2.19, p=0.030).
Older students are less likely to receive parental support and more likely to be supporting
themselves and, possibly, others. They are also more likely to have large expenses such as
home mortgages; hence this difference might be expected.
As the statistically significant factors for older students could be termed “time of life” issues
it is, perhaps, unsurprising that these are all rational decision factors contributing to their
decision to quit. The contributory factors for the younger students were a mixture of both
social integration and rational decision factors and this supports the need to consider both
social and rational factors in understanding attrition.
4.2.3 Open-ended Responses
Once the participants had ranked the statements presented to them in the survey it was
expected that they would then be able to identify what had been the central reason for their
decision to quit. Participants were asked two questions: question 14 asked them to write
about the reason, from those presented in the previous statements, which had the greatest
influence on their decision; and question 15 asked if they had further comments to make
about their main reason for quitting, especially if the statements presented in the survey had
not covered it (see Appendix E – page 9). In the majority of cases, participants did not
identify reasons that were not covered by the statements in the survey and their responses to
question 15 were additional descriptions of the problems they had written about in their
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answer to question 14. Where there were differences these will be noted but, for the most
part, the information gathered from these two questions has been combined and then analysed
using NVivo 9.
The analysis of the written responses, revealing the common themes which have been
classified as Rational (R), Social (S) or a combination (S & R), can be seen in Table L26.
Just under one fifth (25/119) of the written answers indicated that factors related to the degree
or course (MRQ1) had featured significantly in the respondent’s decision to quit. To illustrate
this, the following quotes provide insight into the course content:
“[At my university] IT was presented as highly technical, highly mathematical and very
individualized. In reality, IT has close links with business, work in teams and
programming is a small portion of what IT is about. My IT course placed heavy
influence on programming having them as compulsory subjects. There were no options
for studying other areas as the course structure was simply too rigid.”
“I was currently working in the field and my background knowledge on the subjects was
far beyond the level we were working at. Due to this I found it hard to concentrate and
stay focused.”
“Some of the content seemed irrelevant and we were actually told straight out by the
lecturer that the programming language "Dr Scheme" would be of no use to us later on.”
“Course content is not relevant to real world application and is way out of date.”
A number of respondents (22/119) realised they had taken on more commitments (MRQ2)
than they were able to handle:
“I just couldn’t juggle fulltime work, a family with 3 kids and many other interests and
commitments. I over committed. I still would like to study but not for a while.”
“Too hard to fit the full time work I had at the time.”
“The main reason I ended up having to withdraw was the snowballing effect of my work
life. I had started a business during my first year of study that took off quite successfully
and meant I had less time to focus on my studies. I started failing units, mainly due to
non-attendance and failing exams. As a result, I ended up dropping the course entirely.”
Respondents (14/119) were also critical of their lecturer’s style or competence (MRQ3):
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“I had problems with the lecturers ranging from those who couldn't even speak the
language - I had enormous difficulties in understanding what they were even trying to
say in class - to those who could speak English (native speaker) yet declined to help me
in my assignments.”
“Lecturers have no idea how to create a good learning environment.”
“There was really no help or motivation from the boring lectures... it just went on and on
and when you approached the lecturer, they do sometimes stare blankly at you and try to
explain but comes to no avail.”
Others (12/119) recognised that the course was designed with inherent assumptions about
students’ prior knowledge and experience (MRQ4):
“There was a lot of expectation that students had lots of prior experience in
programming. I realise that while this is true for most students, coming from a non-
programming background the course was pretty intimidating and assignments were
pretty much plunging into the deep end.”
“Lecturers teach you on assumption you have x amount of knowledge in a certain field
when you don't.”
“The way the course was structured and presented to students was poor.”
“I understand that an understanding of how to program is essential to the course, but the
way in which we were taught was uninteresting, and didn't seem like it was leading
anywhere.”
“You needed the Cisco CCNA in order to do one of the units.”
“Lecturers automatically assume you have knowledge in a certain area before you
participate.”
“I didn't have the expected background knowledge; the courses were definitely geared
towards those with more pre-existing knowledge.”
While personal issues (MRQ5) also affected a few (10/119) of respondents:
“I suffer from clinical depression and my issues related to that condition contributed to
my withdrawal.”
“A combination of illness and long hours of travel time resulted in poor academic
performance during my last semester, which at the time made me think I could not either
complete my degree or at least not well enough to be competent.”
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“A family member that has passed away, since that in the past 2 years i have lost 2
relatives overseas which disturbed me with my studies and my family with their jobs.”
“Hubby said tutty bye and I had two young children so had to work.”
“My housemates skipped out on me while owing a lot of rent and I was forced to find full
time work.”
“Moved to Toronto, Canada in 2007.”
Some students (8/119) cited the lack of help as a significant contributor to their decision to
quit (MRQ6):
“Workshops/Tutorials should have a tutoring/teaching component. Being told that I
could only be allocated approx 3 to 4 minutes out of the tutorial isn't value for money.”
“specifically asked admin staff, or teaching staff for help and was turned away on every
occasion (sic), or told to look at a website (neither of which provided the slightest bit of
help).”
“Not enought (sic) help from staff was offered.”
A small number of students (6/119) found the financial difficulties they experienced (MRQ7)
too difficult to overcome or avoid:
“it was just too expensive and couldn't cope.”
“It was extremely expensive commuting to university.”
“Working and affording to live without impacting performance while studying.”
Some students (5/119) acknowledged a loss of passion for ICT (MRQ8). In some cases they
gave some explanation for why they believed that had happened:
“I was not enjoying IT full stop. It was uninteresting and not exciting.”
“I picked the wrong degree because I completely lost passion for it.”
“i lost the drive and interest to try and keep up with the content of the courses.”
“Although I love IT and always thought I'd study it, I decided a degree combined more
with business would be more beneficial.”
“I didn't study hard enough because of lack of passion.”
“I realised that ICT was a hobby and not something I wanted to pursue professionally.”
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A few students (4/119) also indicated their frustration that their prior work or learning
experiences were not recognised by their university (MRQ9):
“I found it very difficult to get acknowledgement of work experience to start at a level
within the course that would have kept me engaged in the assigned material.”
“The lack of RPL was a big problem for me. I'm happy to sit exams to gain the RPL. I'm
not happy to have to use a whole semester and pay many hundreds of dollars to learn
nothing.”
“Where content overlapped with real on the job experience, staff were inflexible and
unwilling to award credit.”
“There is nowhere near enough credit for prior experience and on-the-job learning.”
For a small number (3/119), pregnancy and marriage had intervened (MRQ10):
“Fell pregnant. I had been working full-time & studying 2 subjects a semester but this
became too much.”
“Got married.”
While others (3/119) noted the lack or paucity of alternative learning opportunities (MRQ11):
“The course was offered externally but the materials provided were hopelessly
inadequate.”
“I left the degree because there was no option to do it fully online.”
“Previous experiences with X University external studies had been positive ... the
experience with the IT areas however were on the whole very dissapointing (sic).”
“The course was very inflexible in that there was no opportunity for those in fulltime
work to attend lectures/tutorials or study online.”
Or the restructuring of courses (MRQ12) (2/119):
“Some restructuring in my chosen course was a major contributor to my choice to
leave.”
“The course was not well structured and was undergoing restructuring the year that I
was entering.”
For two students (2/119) the desire for permanent residency required them to look for another
course (MRQ13):
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“Other course (sic) have more opportuniy (sic) to apply for permanent residency visa.”
“For immigration.”
While others (2/119) acknowledged that they had failed to take personal responsibility
(MRQ14) for being a university student:
“I was too young, solialising (sic) too much and generally was unwilling to do the work
required for me to complete university at that time.”
“Couldn't get used to taking personal responsibility for the flexibility that uni offered
coming straight out of high school.”
One student (1/119) acknowledged the part played by their work-placement (MRQ15):
“The decision was made that I didn't enjoy working the field of IT, finishing a full
placement of IBL.” The following are examples of responses given to questions 14 and 15 which cited disparate
reasons for quitting.
• One respondent cited the lack of public transport, and the financial difficulties
associated with driving long distances due to the cost of petrol, in answer to question
14 but mentioned course issues such as lecturer’s teaching ability, English-language
proficiency, fast paced classes, difficult assignments and the lack of help in question
15.
• Although their answer to question 14 was “I picked the wrong degree, it was nothing
like what I expected”, in question 15 the respondent revealed that the lack of social
contact contributed to their decision quit:
“During the tutorials there was no chance or encouragement to socialise with
other students. Making friends in tutorials is one of the most important things a
first year university student can do.”
• Another respondent indicated in question 14 that their depression had caused them to
quit but in question 15 cited problems with their course:
“Some restructuring in my chosen course was a major contributor to my choice to
leave.”
• Although financial problems were stated by one respondent to be the main factor in
question 14, the course structure and lack of help were cited in question 15:
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“The university is well equipped but the course is unforgiving on people who fall
behind, seeking help is also very difficult depending on the lecturer provided.”
Interestingly, some of these examples do justify the asking of two open-ended questions as
there were issues not covered by the statements in the survey. Issues such as the restructuring
of courses and the English-language proficiency of lecturers had not been identified in the
literature review, while most other responses to these questions did identify factors that had
been presented in the statements. While it is useful to note that these new factors exist, none
of them were reported with sufficient frequency to significantly affect the results of the
current research.
Although the most frequently mentioned themes can be classified as rational factors such as
course lecturers, course content, and course structure or design, there were also social factors
such as illness, pregnancy, and an inability to get help when it was required which
contributed to attrition. This result is surprising, however, as it could have been assumed that
there would be more social integration factors cited by the respondents. One explanation for
this could be that presenting students with an opportunity to identify their main reason for
quitting may have resulted in some rationalisation on their part as was indicated in the quote:
1. “real reasons may evade the researcher;
2. the former student may be unwilling to identify the real reason;
3. multiple reasons may exist and it may be impossible to disentangle individual
contributions;
4. reasons may be confused with actions (e.g. “got a job” may be due to lack of money,
lack of interest in the course, as so on).”
(Hoyt cited in Price et al., 1992, p7)
Point two in this quote may be the closest explanation for the level of rationality displayed in
the direct quotes taken from the written responses, as the process of ranking the negatively
worded statements preceding the open-ended questions was intended to allow students to
recall their past experiences and present them with as many relevant issues as was feasible.
Much of their actual day-to-day experiences as students would remain forgotten and only the
most memorable problems would emerge in this process. Most students found issue with
various facets of their experience which were external to themselves (i.e. not their fault) and
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that is not unexpected. Therefore, the expected factors relating to social integration were not
as frequently pointed out unless, of course, they were about other students:
“the other individuals completing the course were below my expectations.”
“I found ... the people (students/my peers) very uninteresting with regard to social
interaction.”
4.2.4 Interviewees and their Written Responses
Given the large number of factors in the questionnaire, it would have been impractical to ask
an interviewee about all the possible factors. However, all the respondents rated the majority
of factors negatively (either strongly disagree or disagree), so only a subset of factors were
rated positively (agree or strongly agree) by each respondent. Subsequently, interviews
conducted with the questionnaire volunteers included further discussion of the specific
questions with which they had agreed or strongly agreed. The written responses to questions
14 and 15 in the survey, and the data collected during interviews with those particular
respondents, will be presented together as there is much similarity in the data gathered by
these methods. There were 16 interviews conducted, five were with female survey
participants and 11 with male participants.
In some instances the interviewees gave additional responses to these questions which were
not anticipated when the questionnaire was designed. These additional responses included
unanticipated experiences and feelings.
4.2.4.1 Male Interviewees
Several male interviewees had agreed with the statement “I was in the minority” mostly
because they were mature-age students and had ‘real world’ experience by comparison to the
majority of their classmates, while two indicated that they were in classes of mostly
international students speaking English as their second language.
One male student (A) agreed that “living at home was too difficult” and “my family didn’t
help me study at home” which might conjure up nagging parents or annoying younger
siblings, but his situation was a combination of the financial demands which were part of his
acquiring “an instant family” as his partner and her young children were dependent on him,
and the demands they put on him to spend time with them, rather than studying:
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“Financial struggle. I was unable to support my family while attending University
Full Time. I tried going part time but this was still too hard. I tried external, however
working full time and then trying to study all became too stressful.”
Travelling considerable distances was something that five male respondents found difficult,
two of whom stated that they could spend more than 10 hours travelling to and from their
universities on any given day:
“Public transport from the southern highlands was almost nonexistent (sic), thus I had to
drive - petrol was costing me greatly. To make the money to get to uni, I had to spend all
my 'spare' time working, which of course meant I had no time for uni. Stress of both
money and failing classes compounded, making both problems even worse.”
“Definitely the commute and transport to university was the main decision to change my
degree. It was extremely expensive commuting to university and also the distance was
vast. Especially with the long study hours required for Computer Science, I just could not
cope.”
While one student (R) who had simply written “I could not find suitable accommodation” in
response to question 14 gave an explanation in his interview that was quite unexpected in that
it involved him travelling from interstate with the promise of accommodation, only to find
that offer rescinded upon his arrival and, without a place to stay – other than the couch
offered as an overnight solution to his dilemma – he had no option but to return home.
For the most part, however, the information provided by the male interviewees in their
answers to questions 14 and 15 was repeated in their interviews. For example, one student
had indicated in the open-ended question about the main reason that he had quit “because of
the lack of decent job oppurtunities (sic). Games Programming is not highly sought after in
Australia.” When interviewed he said that he had undertaken a work placement in a small
games development company. During the time he worked there it became clear that, if he
wanted to pursue a career in this field, he would have to leave Australia, which he was not
prepared or willing to do.
As this example demonstrates, more detail was given, but the reason given in the
questionnaire was given again in the interview. Of course it was possible to ask whether the
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interviewees had spoken to lecturers, tutors or spoken to the general staff about the problems
they were experiencing. In the majority of cases, the interviewees had not communicated
their concerns. Some justified this on the basis that they did not think help was available or
expected their problem could not be solved, while some acknowledged that they ought to
have sought help at the time they were experiencing difficulties. One had taken various
measures, such as reducing their study load over an 18 month period to try and mediate their
problems, but to no avail. These quotes illustrate the level of frustration experienced by some:
“Not enough help from staff was offered.”
“There wasn’t enough help available for me to consider staying in the degree.”
4.2.4.2 Female interviewees
Of the five female interviewees, three had attended their campus while two had been external
and remote students. Like the previously mentioned issues related to restructuring of courses
and lecturer’s English language proficiency, the concerns of remote and external students had
not been covered in the questionnaire and the comments made by these two external and
remote female students also supports the use of two open-ended questions to allow for these
additional concerns and problems to be revealed.
As with the male interviewees, much of the concern experienced by the females had been
expressed in their written responses but the opportunity to gather further details in
conversation still proved a useful exercise.
One female interviewee (N) had agreed that “there were too many distractions preventing me
from concentrating on my studies” and “the course was too competitive” because other
students whom she called “super hackers” spent their time bragging and disturbing others in
labs.
Two female students (C and F) agreed that they “did not fit or belong”. For one (C) it was
because other students were not appealing as potential friends and did not seem interested in
socialising, while for the other (F) it was because she was older than the other students.
This interviewee (F) also agreed that the “teaching environment was not welcoming” and she
“was not encouraged to do well by teachers” because:
“The tutors did not answer most of the technical questions I had regarding the course.
Often questions relating to course content were ignored and only questions regarding
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timeframes for when the work was to be submitted were responded to at all. As a fully
external and remote student with no other access to [university] staff on the whole this
was a severe disadvantage. I had no reason to believe it was any form of prejudice
unless there is a strong disregard for country students as not being worth developing.
Having completed an Environmental Science degree [at the same university], also fully
external and remote, I found the blatant lack of interest in actually providing tuition in
the information technology area very disconcerting. It rendered any attempts to find
solutions or guidance in areas of appropriate study too difficult and so there was no
point in proceeding with the course.”
Another female interviewee (S) had disagreed with all the statements except two: that “a
family member died or was very ill or had a serious accident” and that she had “picked the
wrong degree”, and had written about her degree choice being influenced by her employer. In
her interview she explained that she had been working in IT as a client liaison officer and had
been told that advancement would come through acquiring a degree. Her employer had
offered to support her studies financially and she had undertaken an IS degree with sociology
as her elective but enjoyed sociology so much that she had transferred to a sociology degree
with an IS major. Her employer informed her that she would not be supported in this degree
and so she had quit.
In her interview the fifth female (K) revealed that she had been disheartened by the fact that,
although she “worked with computers every day”, her classmates had seemed to be far more
knowledgeable than she and possessed a greater understanding of the subjects. As she was a
mature age student, “in the minority” in her classes, and “did not make friends with [her]
classmates”, it is hardly surprising that her feeling that “the course was too fast paced” and
“did not have a workplace” or “business focus” lead her to believe that she had “picked the
wrong degree” and that it “failed to meet [her] expectations”. She had also “become
pregnant” and this was the only circumstance featured in her written response to question 14:
“Fell pregnant. I had been working full-time & studying 2 subjects a semester but this
became too much.”
The interviews with some ex-students did not reveal anything more about their experience
than they had written about in their open-ended responses, while others proved quite eye-
opening. In some cases, this was a result of the unexpected nature of their difficulties, such as
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the male student with the “instant family”, the male student who found the promise of
interstate accommodation rescinded upon his arrival and the male student who had travelled
an inordinate distance to get to his university. The female students, by contrast, had not had
quite such diverse experiences but had found the teaching environment and the attitude of
teachers and classmates unappealing and even hostile. In reality, the individual experiences of
the male students could not have been mitigated to much extent by their university, while
some of the experiences of the female students could, as they involved the teaching practices
of the people involved in running their courses. Better control of students in classes and
giving an equal amount of attention to each person in the classes, as well as in responding to
email enquiries, could have resulted in some of the female students persisting in their
degrees.
4.2.5 Factors Contributing to Attrition
In the conclusion to section 4.2.1, twelve contributory factors, which were rated positively
(agree or strongly agree), were identified by 33.3% or more of respondents. If the data
gathered by the questionnaire was only analysed across the whole sample, issues relevant to
particular groups of students would not be identified as they would be part of a homogenous
mass dominated by the characteristics of male gender, domestic residence and full-time study
load. As there are likely to be contributory factors identified by particular groups, the data
was disaggregated to reveal that, of the 154 participants who provided demographic data: 73
were of traditional age (Trad) and 81 were mature-age (Mat); 29 were female (F) and 124
were male (M); 104 had a full-time study load (FT) and 39 were part-time (PT); and 126
were domestic (Dom) and 10 were international (Int). The responses from each of these
groups were examined to see if any factors significantly affected that group.
As explained previously, a cut-off point of 33.3% agreement was used to identify
contributory factors across the whole sample. It was decided to use the same cut off point for
sub-groups i.e. any factor with a positive (agree or strongly agree) frequency of 33.3% or
greater by any sub-group would be deemed to be a contributory factor for that group. Not
surprisingly, many of the contributory factors for a sub-group had already been identified as a
contributory factor for the whole group. Table 4.27 shows the complete set of contributory
factors, for either the whole sample of for some sub-group. Factors which are only
contributory for one or more sub-groups are shown in bold; contributory factors for the whole
group are shown in plain text.
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Table L28 shows the 4 main sub-groups described above and the percentage of respondents
in those sub-groups who rated the factors positively. Where the proportion of positive
responses in a sub-group exceeds 33.3%, the percentage is shown in bold. These statements
were then categorised as being either rational (19) or social (7) reasons for deciding to quit.
Those classified as social are indicated by “(S)”.
Naturally, if a factor is deemed to be contributory for one of the more dominant sub-groups, it
is almost certainly a contributory factor for the whole group. For example, “course
expectations unmet” was a contributory factor for 64.3% of male respondents and, since male
respondents dominated the sample; it is necessarily a contributory factor for the whole
sample. Nonetheless, 14 additional contributory factors were added, specifically in response
to a high rating by only one or two sub-groups (Table L28).
It is important to include these contributory factors for the sub-groups of students who
participated in the questionnaire because the intent of this study is to compile a
comprehensive list of factors. To do this it is necessary to consider the factors that lead
females, part-time students, mature age students and international students to quit. Identifying
the factors that had the most significant affect on quitting for all types of students means that
this objective has been satisfied.
4.3 ACDICT and ICT Industry Data
While the preceding sections have identified many of the contributory factors leading to
attrition, this is not particularly helpful unless corresponding strategies can be identified. The
literature review did identify a set of strategies, however, it may be an incomplete list or they
may not be used in practice, so two distinct groups of people associated with ICT were
surveyed to identify commonly used or well-recognised strategies. These two groups were:
the Australian Council of Deans of ICT (ACDICT); and the Australian Computer Society. To
distinguish between these groups of people, the first will be referred to as “members of
ACDICT” and the second will be called “members of industry”.
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4.3.1 Members of ACDICT
This section is divided into 2 sub-sections corresponding to the predominant types of data
used in each sub-section. The first section will examine the quantitative questionnaire data,
augmented with qualitative data. This section will be followed by discussion of the responses
provided to the open-ended questions in the questionnaire.
4.3.1.1 Questionnaire Data
The survey of members of ACDICT was designed to fulfil a number of requirements of the
overarching ALTC project described in section 1.5. As a result, some sections of that
questionnaire have been omitted from consideration while others, though not obviously
relevant to the objectives of this research, have been included to facilitate an, albeit brief,
insight into the prevailing culture in ICT faculties in Australian universities (see Appendix I).
The first contact with the members of ACDICT was via a letter and printed questionnaire sent
to them by mail but they were also given the option to complete the questionnaire online via
the SurveyMonkey website. A total of 46 responses were received. ACDICT members were
asked about: the numbers of students, and particularly of women, enrolling in their ICT
degrees; the outreach activities of their faculty; and their thoughts on a gender-inclusive
curriculum. Questions were also posed about work integrated learning (WIL) and the
teaching-research-industry-learning nexus (TRIL) but these will not be considered in the
following discussion.
It should be noted that, although 46 questionnaires were received, not all questions were
answered and not all statements were ranked, so there will be disparities between the number
of questionnaires and the numbers of responses.
The first question asked respondents to indicate whether the number of students enrolling in
their degrees was: Steady; Falling; or Increasing. The number of responses for each of these
choices is presented in Table L29.
The second question asked about the status of domestic undergraduate female enrolments in
their degrees. The number of responses for each of these choices is presented in Table L30.
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Four of the respondents did not complete this question but elected to write: “don’t know”,
“unsure”, “unknown”, or “not a metric that we monitor”, while two who chose ‘Steady’ also
noted: “and very low”; and “but very, very low <5%” female enrolment.
The notes made about the steady but low numbers of female enrolment are in line with those
of the government statistics (see Figures 2.4 and 2.5).
The third and fourth questions were in the form of a statements to be ranked on a 5-point
Likert scale ranging from strongly disagree (SD) to strongly agree (SA) and an open ended
question. Question 3 stated “We are trying to increase ICT female enrolments” while
Question 4 stated “Our strategy for increasing female enrolments in ICT is effective”. The
responses are shown in Table L31.
Two respondents elected to add: “N/A” next to question 4; and “though low priority” next to
questions 3 and 4 while a third did not complete question 4 and wrote “unsure” having
selected strongly agree for question 3. The rankings indicate that most respondents (76.1%)
believe there are efforts to increase female enrolments while 52.3% indicate that the strategy
being used is not effective. The written responses to questions 5, 6 and 7, presented later,
provide information about the strategies commonly employed and this will serve to explain
why more than half the respondents chose to disagree with statement number 4.
Five statements on a gender-inclusive curriculum were presented for respondents to rank.
These were:
• “We are unsure of what a gender-inclusive ICT curriculum would really look like” • “An ICT curriculum that appeals to women would be different to one that appeals to
men” • “We make an effort to have an ICT curriculum that is explicitly gender-inclusive” • “There is a link between having a gender-inclusive curriculum and the low proportion
of women studying ICT” • “We would welcome informed guidelines on the practical implementation of a
gender-inclusive ICT curriculum”
The frequency of their rankings is presented in Table L32.
Five respondents chose to add a comment to question 11. Two added “unknown” and
“attrition is high and a link is to be tested” while three did not rank question 11 but wrote a
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question mark instead. It appears that the majority of respondents were unwilling to make a
link between a gender-inclusive curriculum and the number of women enrolled in ICT. This
is a reasonable position to adopt since the survey did not provide an outline or information
which described what was meant by “gender-inclusive curriculum” and participants may
have been loath to attribute the “low proportion” of female students to its existence or lack
thereof. However, interestingly, one participant did indicate their belief that a link between
attrition and gender inclusivity had not been tested. This could suggest that the respondent
questioned whether a non-inclusive curriculum resulted in attrition of women from ICT
courses.
4.3.1.2 Open-ended Question Responses
Questions 5 and 6 asked participants to write about their faculty’s current strategy for
increasing the enrolment of women in ICT and what additional activities could be
undertaken. Interestingly six respondents indicated that there were no strategies, while three
left the space blank. The other responses to these questions are discussed in later.
Question 7 invited participants to write about the additional strategies they could suggest,
apart from those already mentioned, that would help attract more women into ICT. Table L33
contains the most frequently suggested strategies.
There were responses which were not able to be categorised, but provide interesting insights
into the views of participants. One respondent commented:
“We really only get to speak to high school students who have 2 years or more to
possible uni entrance. There seems to be an issue communicating with these females
after that.”
Taking this comment at face value, it appears that the respondent may be critical of the
approach taken to communicating with potential students: that there is little outreach, that it is
possibly too late, and that there is no regular contact with potential female students.
Interestingly, the body that represents the ICT industry in Australia – the ACS – also attracted
some criticism based on the content of its promotional material with this comment
highlighting a problem that may not have been noticed by many in industry:
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“Establish national role models and videos, the ACS Foundation one at present is all
men!”
The comments of others embodied some of the fundamental difficulties to be faced in trying
to enact change:
“My perception is that women are more comfortable using IT than with building,
analysing etc IT systems, which appears to be a more male-centric view.”
“Find areas that are attractive to females (are there any?), use these in marketing
and up front in subject names and degree names.”
As the Dean sets the tone for his or her faculty (and the majority of Deans of ICT in Australia
are male), these stereotypical views of female involvement in ICT indicate that further
gender-based understanding of the sector is necessary. As the answers to question 13 will
demonstrate, the majority of respondents do hold stereotypical views of women as shown by
the frequency with which they wrote about features of an ICT curriculum that they believe
are appealing to females (Table L34).
There are also a significant proportion of respondents who indicate they do not even possess
this knowledge, while one participant appears to make a valid point, but follows it with a list
of stereotypical female interests:
“The application of curriculum should be gender sensitive not the curriculum. It is
important to show that the curriculum does what female students are interested in. It
includes providing welfare, health, community support and similar.”
And others also nominate ‘soft’ skills, ‘nurturing’ and specifically state that programming is
not attractive to females:
”Soft skills, business and communication-oriented, a more ‘liberal arts’ style
curriculum”
“Team work based assessment; nurturing and supporting”
“Collaborative work, softer areas (db, analysis/design, etc) usually not
programming”
Three participants, however, did make similar, and valid, criticisms of the question:
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“Women this is not a single blob – many differences between women – do you
mean the women we currently attract, or those we would like to attract?”
“Some features appeal to some women. I find it difficult and dangerous to
generalise.”
“Difficult (impossible?) to generalise…”
This reluctance to generalise was also echoed in responses to question 14 which asked
participants to nominate the features of the ICT curriculum that appeal to men.
“The same as for girls – the curriculum should simply be engaging and allow the
inclusion of personal preferences of the individual
“Not very sure that we should be focussing on gender differences – there is such
variability in what attracts students. A broadly inclusive curriculum that includes
activities such as business studies, social benefit, multimedia and media design, soft
skills, etc will attract a broader range of students, including females.”
, not based on sex.”
Table L35 contains the most frequent written responses about features of ICT that are
attractive to men. As with the question on features appealing to women, a significant number
of respondents did not know what would appeal to male students, either, and the majority of
nominated features were also stereotypical.
The final question asked of the ACDICT members was about the measures that have been
taken to ensure the ICT curriculum is gender-inclusive. The majority of respondents indicated
that they did not know of any measures or that there were no measures taken in their faculty
to ensure that the curriculum did not exclude people based on their gender (Table L36).
This result is supported by some of the written responses, one of which admits:
“No real measures have been taken. The view may be seen as protecting our ‘core
constituency’ which (at our university) is 90% male. There are clearly risks in
alienating this constituency, just as there are (potential) benefits in more aggressively
targeting female applicants.”
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This comment clearly indicates a belief that the current curriculum suits the male students
who are currently attracted to the course and suggests that a gender-inclusive curriculum may
be seen as too ‘soft’ for their particular cohort.
Another respondent points to an attempt to be inclusive of all people classified as minorities,
as well as women, and suggests it has enjoyed some success:
“In trying to be culturally inclusive I believe we are improving our gender
inclusivity.”
4.3.2 Members of the ICT Industry
This section is divided into 2 sub-sections corresponding to the predominant types of data
used in each sub-section. The first section will be a statistical analysis of the questionnaire
data. This section will conclude with a discussion of the responses provided to the open-
ended questions in the questionnaire.
4.3.2.1 Questionnaire Data
The survey of industry members was also designed to fulfil a number of requirements of the
wider investigations of the ALTC project briefly described in section 1.5. As with the survey
of ACDICT members, the survey of industry also included sections not pertinent to the
current research, while others that are not obviously relevant have been used (see Appendix
J). In much the same way as those for the Deans, the responses given provide insights into the
culture prevalent in the ICT industry and this allows the responses to the pertinent questions
to be better understood. Both the ACS and Advisory Board members were presented with an
identical questionnaire which comprised: questions requiring a written answer; and
statements to rank on a five-point Likert scale with choices ranging from strongly disagree to
strongly agree. The areas covered in the questionnaire were: gender issues; perception issues;
work integrated learning (WIL); and the teaching-research-industry-learning nexus (TRIL).
WIL and TRIL will not, however, be considered in the following discussion. In total, 132
(20.5% female, N=27) ICT industry members participated but, it should be noted that not all
participants answered all questions, resulting in discrepancies.
Four statements about the culture of workplaces were presented for industry members to rank
and the frequency of their choices are shown in Table L37. Interestingly, more than half the
respondents agreed or strongly agreed that ICT is culturally male-oriented, while also
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acknowledging that women’s views and input were not fully embraced, even though the
majority believed those perspectives and approaches were valuable. As only 20% of the
respondents were female, this suggests that the stereotype about ICT being a male-dominated
field, and by extension the ICT industry, does not appear to be very far off the mark. It is
reasonable to expect that those who perceive these cultural issues negatively convey these
feelings to others outside their workplace which contributes to or supports the negative
perceptions which may be held by members of the community about ICT as an attractive
study and career choice.
To discover whether this is true, 9 statements on perceptions were also presented for industry
members to rank and the frequency of their choices are shown in Table L38. The responses to
these statements also confirm much of what is suspected about external and internal
perceptions of the ICT industry. Almost half the respondents agreed or strongly agreed that
the public perception of the industry was negative while more than 50% indicated that they
believed that improved professionalism, professional accreditation and clear distinctions
between occupations and careers would improve public perception. Interestingly, although
more than 50% agreed that the ICT industry tried to improve the public perception and more
than 40% agreed that their employer tried to do this as well, less than 12% thought the
industry’s efforts were successful and only 21% thought their employer had succeeded in this
effort.
4.3.2.2 Open-ended Question Responses
Industry members were also asked to respond to an open-ended question about how
universities should prepare students for the workplace. This question elicited some lengthy
responses which give insights into both the workplace and also the ICT industry though most
did not explicitly give an answer to the question. The insights provided were, in some cases,
quite shocking. One respondent wrote:
“If preparing students for the workplace you MUST tell them to expect to be bullied,
harassed, abused, intimidated, assaulted, ignored, discriminated against and
persecuted by their employers. This is especially true of any publicly funded
institution.”
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There were respondents who did answer the question about ways to prepare students for the
workplace and they made suggestions about fundamental aspects such as:
“Give the students advice in CV preparation and interviewing skills.”
“If you can set some expectations about work ethics eg arriving on time, asking
permission to perform tasks and roles. We do require a level of commitment and
integrity.”
Others noted a commonly cited issue:
“Developing good communication, negotiation, presentation skills is a good start.
Learning how to deal with conflict is also important and understanding ‘people’ and
being able to relate to them in general goes along way. Soft skills stuff.”
“Good general communications skills is the best preperation (sic).”
Some respondents indicated that both male and female students required additional education
on how to avoid or deal with sexist attitudes and behaviour:
“Encourage awareness of gender issues and non-sexist behaviour.”
“Women need to have opportunities to develop confidence and poise in the workforce,
and learn techniques for dealing with difficult attitudes from male co-workers. In
addition, males going into the ICT industry need to become more conscious of how
their attitudes can hamper the progression of women in the ICT workforce (such as
derogatory gender-related comments etc).”
“Students should be helped to define and establish the boundaries for non-
discriminatory behaviour. Additionally, to be given tools and mechanisms to counter
this behaviour when it is encountered.”
Industry members were also asked to respond to a question about the ways in which the
public’s perception of ICT could be changed:
“Needs a marketing approach in conjunction with Cleo, Dolly and other magazines to
redefine the view younger girls have of appropriate career paths for girls, more
success stories in these magazines … and TV shows, movies etc – then wait a
generation! So – Universities could get their graduate management school along with
psychology and IT to develop a strategy and then sell it to the major publishers –
needs a coordinated approach across Australia so fund a campaign manager etc.”
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The sentiments expressed by many were, however, not overly optimistic. Eight participants
admitted that they did not know or could not think of anything that would help and others
were equally negative in their answers:
“We talk about it but don't do much that really makes a difference.”
“I can’t see industry either harnessing the positives or responding effectively to the
negatives.”
“Even Government needs to address this issue, and their lack of support to
organisations like WITWA [Women In Technology Western Australia] is indicative of
a need for change.”
The views presented in this section, and the workplace culture that has formed them, cannot
be dismissed and must not be regarded in isolation. The sentiments expressed indicate that
there are valid concerns about the ICT industry as individuals experience it, and they must
make their experiences and feelings known amongst their friends and family. Students who
become aware of these cultural practices and norms while they are studying may believe that,
despite their abilities and interests, this is not an industry in which they will thrive.
4.4 Conclusion
In this chapter both the qualitative and quantitative student data were analysed using
statistical and heuristic analysis to ascertain which factors were identified most frequently as
contributing to attrition from ICT degrees. This was achieved by considering not only the
factors identified as contributing to attrition for the dominant group (male, domestic,
traditional age students) but also for significant dichotomous groups such as those defined by
their gender, age, study load and or usual domicile. Consideration was also given to the
responses students wrote for the open-ended questions and to the further explanations given
by those who participated in interviews. The chapter ended with the presentation of written
responses by members of both ACDICT and the ICT industry which gave insights into the
views held in academia and the experiences of former students working in ICT workplaces.
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5 FINDINGS
5.1 Introduction
Much research ends when the major factors contributing to attrition have been indentified,
though researchers do offer recommendations and sometimes go so far as to promote
programs or strategies they believe would ameliorate the issues that have been identified, as
is the case for many of the previously discussed researchers.
Throughout this research the central theme has been that of using the socio-rational approach
to understand attrition from ICT degrees. The intent of this research is to produce as
comprehensive a list of strategies as possible from the literature and from ACDICT and
industry questionnaire responses and to:
• critically assess those strategies to determine their efficacy;
• categorise those strategies as social or rational or both
• map those strategies to the focus or expected outcomes and the contributory factors
using the socio-rational approach.
Table 5.1 contains the strategies suggested by the literature discussed earlier, mapped to the
expected outcomes or the focus of each strategy. Each of the outcomes or focus has been
classified as Social (S), Rational (R) or a combination of Social and Rational (S&R).
Strategy S R S&R
Female industry mentor for female students (Muller, 1997)
Provide guidance Provide advice Provide support Provide encouragement
Provide access to professional networks Provide insight into industry culture
Male industry mentor (McInnis et al., 2000a)
Provide emotional support Provide psychological support
Provide assistance with career Provide assistance with professional development Provide role modelling
Peer mentor (Lee & Bush, 2003)
Provide guidance Provide advice Provide support
Provide insider knowledge Assist in small group learning
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Small group learning (Cartney & Rouse, 2006)
Promote integration by providing social contact Promote integration by counteracting fragmented experience for non-traditional students Promote integration by counteracting fragmented experience for mature-age students
Promote progression of students Promote retention of students
Foster student potential Promote integration by providing valuable skills to traditional students
Relationship building with cohort by administrators (Brier et al., 2008)
Demonstrate institutional commitment Personalise the institution Foster institutional affiliation Ease transition
Identify problems/potential problems
Foster student socialisation (Bruning, 2002)
Be welcoming to students Run small group tutorials
Use innovative teaching
Establish learning support centres
Modify ICT prerequisites (Margolis & Fisher, 2002)
Remove prior programming experience
Broaden admissions (Margolis & Fisher, 2002)
Include leadership as a candidate characteristic Include community contribution as a candidate characteristic
Connect with high schools (Blum & Cortina, 2007)
Improve high school teacher’s skills Improve high school teacher’s knowledge Highlight failings in classroom management
Identify high-risk courses (Blanc et al., 1983; McInnis et al., 2000a)
Address causes of high risk Provide resources to counteract high risk Reduce withdrawals
Monitor ‘at risk’ students (Rickinson & Rutherford, 1996)
Identify struggling students immediately Reduce student stress
Identify course structure issues
Recognise educational differences (Lewis et al., 2007; Powell, 2008, Roberts et al., 2011a; Roberts et al, 2012)
Provide appropriate course pathways for students
Table 5.1 Strategies to reduce attrition identified in the literature mapped to their expected outcome(s) and indicating whether the strategy is Social (S), Rational (R) or both (S&R)
Table 5.2 summarises the findings from the literature, maps the strategies and their focus or
outcomes to the contributory factors identified in the current research and classifies them as
Social (S), Rational (R) or both (S&R):
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Strategy Focus/Outcome Contributory Factor(s)
Mentor (Muller, 1997; McInnis et al., 2000a; Lee & Bush, 2003)
Provide guidance Provide advice Provide support Provide encouragement Provide access to professional networks Provide insight into industry culture Provide assistance with career Provide assistance with professional development Provide role modelling Assist in small group learning
Didn’t feel I fit in or belong No or few females in classes Didn’t make friends Course lacked workplace focus Course lacked business focus Picked wrong degree Didn’t understand concepts Didn’t understand terms Course too theoretical Course too mathematical Results not as expected I lacked expected knowledge
Small group learning (Cartney & Rouse, 2006)
Promote integration by providing social contact Promote integration by counteracting fragmented experience for non-traditional students Promote integration by counteracting fragmented experience for mature-age students Promote progression of students Promote retention of students Foster student potential Promote integration by providing valuable skills to traditional students
Didn’t feel I fit in or belong No or few females in classes Didn’t make friends Teaching pace too fast Teachers didn’t explain exercises Didn’t understand concepts Didn’t understand terms Course too theoretical Course too mathematical Results not as expected Didn’t enjoy classes Boring classes In minority in class Not encouraged by Teachers Too many distractions
Relationship building with cohort by administrators (Brier et al., 2008)
Demonstrate institutional commitment Personalise the institution Foster institutional affiliation Ease transition Identify problems/potential problems
Didn’t feel I fit in or belong Results not as expected Couldn’t get help
Modify prerequisites (Margolis & Fisher, 2002)
Remove prior programming experience
Didn’t feel I fit in or belong No or few females in classes Didn’t make friends In minority in class
Broaden admissions (Margolis & Fisher, 2002)
Include leadership as a candidate characteristic Include community contribution as a candidate characteristic
Didn’t feel I fit in or belong No or few females in classes Didn’t make friends In minority in class
Connect with high schools (Blum & Cortina, 2007)
Improve high school teacher’s skills Improve high school teacher’s knowledge Highlight failings in classroom management
No or few females in classes In minority in class
Identify high-risk courses (Blanc et al., 1983)
Address causes of high risk Provide resources to counteract high risk Reduce withdrawals
Picked wrong degree Course expectations unmet Teaching pace too fast Didn’t understand concepts Didn’t understand terms Course too theoretical Course too mathematical Didn’t enjoy classes Boring classes Not encouraged by Teachers I lacked expected knowledge In minority in class
Monitor ‘at risk’ students (Rickinson
Reduce student stress Identify struggling students immediately
Didn’t feel I fit in or belong No or few females in classes
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& Rutherford, 1996) Identify course structure issues
Results not as expected Picked wrong degree Didn’t understand concepts Didn’t understand terms Course too mathematical Course too theoretical Couldn’t get help In minority in class Didn’t enjoy classes Boring classes Too many distractions
Recognise educational differences (Lewis et al., 2007; Powell, 2008; Roberts et al., 2011a; Roberts et al., 2012)
Provide appropriate course pathways for students
Didn’t feel I fit in or belong Didn’t understand concepts Didn’t understand terms Teaching pace too fast Didn’t enjoy classes Course lacked workplace focus Course lacked business focus Course too theoretical Course too mathematical Boring classes Teachers didn’t explain exercises Not encouraged by Teachers In minority in class
Table 5.2 Strategies to reduce attrition identified in the literature and their focus or expected outcomes mapped to the contributory factors identified in this research Most of the contributory factors in Table 5.2 can, in fact, be addressed by more than one of
the strategies suggested in the literature. For example, a student who agreed with the
statement “I didn’t feel I fit in or belong” could be assisted by 7 of the strategies:
• having a mentor;
• participating in small group learning;
• broadening of the admissions criteria;
• modifying prerequisites for the course;
• systematic monitoring of ‘at risk’ students;
• and the recognition of the educational differences of students and the provision of an
appropriate course.
This indicates that universities do not necessarily have to introduce a raft of changes.
Focusing on several vital areas will not only benefit sub-groups of students but will improve
the learning and university experience for all students.
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The following two sections present information about the strategies which have been tried or
continue to be used in universities around Australia and the strategies that have been
suggested, or were experienced, by current ICT industry members.
5.1.1 Strategies Suggested by Members of ACDICT
Table 5.3 is a frequency distribution table of the responses from the members of ACDICT.
Each of the suggested strategies will be discussed in the order in which they appear. It should
be noted that the following strategies focus specifically on females because that was a
requirement of the ALTC project grant. However, as one ACDICT member pointed out, a
more inclusive curriculum is more inclusive for everybody, so perhaps strategies for girls will
have a broader appeal.
Suggested strategy No. Emphasise future careers for female students (R) 7
Provide role models/mentors for female students (S & R) 6
Run camps or enrichment programs for high school students (S & R) 6
Establish women-specific clubs (S& R) 6
Run women-specific events (S & R) 6
Offer financial incentives/assistance to female students (R) 4
Table 5.3 Strategies suggested by ACDICT to attract and retain students
The most frequently suggested strategy was to ensure that young women were cognisant of
the range of future careers available to them. There is some support for the belief that
students are not well informed about ICT jobs (Beyer et al., 2003; DCITA, 2006; von Hellens
& Nielsen, 2001) or have decided against a career in ICT due to enduring negative
perceptions:
• that it would mean sitting alone at a keyboard all day long (Biggers et al., 2008; Dee
& Boyle, 2010; Frieze, 2005);
• that it is men’s work (Clayton et al., 2009; Crump et al., 2007; Dee & Boyle, 2010);
or
• that there are few jobs left since the ‘dot com crash’ (Blum & Cortina, 2007; Craig,
2010; Katz et al., 2006).
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Despite concerns about the underlying reasons for suggesting that this is a problem, it is vital
that unambiguous information about the variety of roles across all commercial enterprise is
clearly conveyed to all students.
Not only do potential students need to be made aware of the diverse employment prospects in
the ICT industry but so do school careers advisors, teachers and parents: all of whom are
particularly influential when students are making decisions about their future career direction
(Barker, Snow, Garvin-Doxas & Weston, 2006; Kenwright, 1996; Madigan, Goodfellow &
Stone, 2007; Young, 2003).
5.1.1.1 Suggested ACDICT Strategies Mapped to Contributory Factors
The six strategies suggested by ACDICT members in Table 5.4 have both social and rational
aspects. Three can be categorised as purely rational: emphasising future careers; and offering
financial incentives or assistance, while the other five are a combination of social and
rational: providing role models or mentors; conducting outreach to high school students via
camps or enrichment programs; and targeting potential female students with women-specific
events and membership in women-specific clubs.
Strategy Focus/Outcome Emphasise future careers (R) Provide direction and an end goal
Role models (S&R) Provide incentive to continue
Mentors (S&R) Provide support to continue
Camps or enrichment programs for high school students (S&R)
Provide opportunity to participate Provide opportunity to try Clarify requirements of ICT degrees Clarify requirements for success
Women-specific club (S&R) Provide opportunities to socialise Provide opportunities to lead Provide additional learning opportunities
Women-specific events (S&R) Provide opportunity to socialise Provide direction and end goal Provide incentive to continue
Financial incentive (S&R) Provide incentive to participate
Financial help (R) Provide incentive to continue
Table 5.4 Strategies suggested by ACDICT mapped to the focus or expected outcome and classified as Social (S), Rational (R) or both (S&R)
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The final step in this process is to map these strategies to the factors contributing to attrition
using the socio-rational approach as demonstrated in Table 5.5:
Strategy Focus/Outcome Contributory Factor(s) Emphasise future careers Provide direction and an end goal
Picked wrong degree Course lacked workplace focus Course lacked business focus Course lacked practical applications
Provide role models Provide incentive to continue
Picked wrong degree Course lacked workplace focus Course lacked business focus Course lacked practical applications
Provide mentors Provide support to continue
Too many distractions Picked wrong degree Course lacked workplace focus Course lacked business focus Course lacked practical applications Not encouraged by Teachers
Run camps or enrichment programs for high school students
Provide opportunity to participate Provide opportunity to try Clarify requirements of ICT degrees Clarify requirements for success
I lacked expected knowledge Didn’t understand concepts Didn’t understand terms Course too theoretical Course too mathematical I didn’t feel I fit in or belonged
Women-specific club Provide opportunities to socialise Provide opportunities to lead Provide additional learning opportunities
No or few females in class Didn’t feel I fit in or belonged Didn’t make friends Course lacked workplace focus Course lacked business focus Course lacked practical applications In minority in class Couldn’t get help
Run women-specific events
Provide opportunity to socialise Provide direction and end goal Provide incentive to continue
No or few females in class Didn’t feel I fit in or belonged Didn’t make friends Course lacked workplace focus Course lacked business focus Course lacked practical applications In minority in class
Financial incentive Provide incentive to participate
Didn’t make friends Uni too expensive In minority in class
Financial help Provide incentive to continue
Uni too expensive Conflict with work Timetable conflict with work
Table 5.5 Strategies suggested by ACDICT mapped to attrition contributory factors
This mapping demonstrates how particular strategies could minimise or neutralise some of
the problems specific to female students. Issues such as their low numbers, making friends in
class and getting the help they need could be alleviated by forming a women-specific club
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which could also provide them with opportunities to learn about career paths and types of
jobs open to graduates, filling some of the knowledge gaps such as the workplace, business
and practical aspects not currently achieved by their courses.
Connecting all students to academic mentors would also be beneficial as they would provide
additional support and encouragement when students begin to feel overwhelmed by their
commitments and start to question whether their performance is acceptable, or if they have
chosen the right direction. A mentor from industry, on the other hand, would not only be able
to keep their mentee on track but could also provide them with insights about their own job
and workplace, giving students an opportunity to grasp the bigger picture and see how their
skills will fit into the real world of work after they graduate. For those students whose
situation makes it imperative that they work while studying, greater financial help would also
be beneficial in alleviating their need to spend hours in employment and allow them to devote
a greater amount of their time to their studies.
5.1.2 Strategies Suggested by Members of the ICT Industry
Table 5.6 is a frequency distribution table of the responses from the members of industry.
Each of the suggested strategies will be discussed in the order in which they appear.
Suggested strategy No. Encourage awareness of/change negative behaviour/attitudes of male students and academics
and equip students with necessary skills (S & R)
9
Provide examples to female students of real-world benefits and careers provided by ICTs (R) 4
Provide role models/mentors to female students (S & R) 3
Offer new or modify existing courses to attract female students (R) 2
Establish a women’s network (S & R) 1
Give first priority to women entrants (R) 1
Promote a positive view of ICT via the popular media (R) 1
Table 5.6 Strategies suggested by ICT industry to attract and retain students
As discussed in Chapter 2, the WIT@Swinburne network was established in order to ensure
that female students had an awareness of and could participate in addressing negative
behaviour and attitudes which can be present in a male-dominated discipline. As well as
countering the stereotypical beliefs about who is suited to the ICT field, the network also
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presented female students with opportunities to learn about the successes of women already
working in the ICT industry as well as forming friendships with each other.
The sense of belonging that can be created by socialising and studying with friends can be
further enhanced with the addition of industry (Muller, 1997), academic (Lee & Bush, 2003)
or peer (Cohoon, 2001) mentors who can provide students with a number of benefits that
assist them with both their understanding of the roles available to them once they graduate
(McInnis et al., 2000a) as well as with their more immediate needs while studying (Connolly
& Murphy, 2005).
The Carnegie Mellon experience has demonstrated that expanding the net, and adding aspects
to the curriculum to make it more interesting to a wider group of students, enhances its
attraction and increases the number of people with a wider range of interests and this, on its
own, changes the atmosphere of a once male-dominated environment (Blum & Frieze, 2003,
2005; Frieze, 2005; Frieze et al., 2006; Frieze & Quesenberry, 2013; Frieze et al., 2011).
However, all that encouragement may go for nought unless specific attention is paid to the
content and methods used in teaching computing. As Mahony and Van Toen (1990) point
out, the way in which teaching is conducted is viewed by many as unproblematic, while they
would argue that the opposite is true. They suggested that the fairly recent establishment of a
recognisable canon in these new subjects was a major cause of the increased gendering of
professional computing. Some researchers (Hanappi-Egger, 2012; Heemskerk et al., 2005;
Koppi et al., 2010b; Mills et al., 2008) have advocated the need for change to the teaching
canon in order to create a more ‘female-friendly’ or ‘inclusive’ environment as they
recognise the effects the male dominant view has on the curriculum. Others (Beyer et al.,
2003; Blum & Frieze, 2005; Boivie, 2010; Craig, 2010), however, warn that this may have
the opposite effect as the redesign of the curriculum simply reinforces existing stereotypes
about the ‘natural’ interests and behaviours of women. This is supported by the wording of
the strategies suggested by members of industry:
“Encourage participation between highly Male and highly Female oriented courses
(ie. Nursing and Engineering) by offering a project requiring the design of a HMI for
a Nursing Application, for example.”
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“Universities should re-think what they offer students and how they focus their
courses and take into consideration how women approach work/life. Web
Technologies, for instance, instead of 'programming'. Web Technologies allows for
content related to social networks and connectivity. Collaboration is much more a
female trait and that should be much more incorporated to keep the courses attractive
to females (and hopefully change the work place culture over time).”
“University computing degrees should be modified to take into consideration the split
in industry between architecture and engineering, those people who have very strong
interpersonal communication, requirements gathering, system visualisation, etc,
skills. If an IT architecture degree was offered I believe it would attract females.”
If a concerted effort is to be made to change the current imbalance in the gender ratios of
those enrolled in ICT degrees (see Figure 2.4) and subsequently employed in the industry,
then these types of arguments are likely to be made for and against such affirmative action, as
this quote from an ex-ICT student makes clear:
“Where was my special help? Where was my motivation? Where was a dedicated
body to support me? Like women in ICT? Women this, women that. All this in my face,
from uni, from state and local governments....Federal minister for women, breast
cancer this and that...there are many different types of cancer that receive a 10th of
what breast cancer does. Sexism? Never witnessed it, not once in 3 years. Its (sic) all
in your head, ladies. They are not out to get you. As a male, I felt like I was forgotten
about, not cared about, someone who would just make do and thrive...which I didn't.”
Male, 19, IT
Affirmative action such as making the entry of female students the first priority does not
always have the desired effect, as the last quote above indicates and there is the danger that it
will support the suppositions about the ‘differences’ between men and women which has
more chance of reinforcing stereotypes (Blum & Frieze, 2005; Craig, 2010, p15), rather than
removing them.
5.1.2.1 Suggested Industry Strategies Mapped to Contributory Factors
Table 5.7 shows the seven strategies suggested by industry members which are also a mixture
of social and rational, with three being a combination: changing the attitudes and behaviour
of students and academics; providing role models or mentors; and establishing a women’s
138
network, while four are rational: providing real-world examples and benefits of an ICT
career; offering new or modified ICT courses; giving first priority to women entrants; and
media promotion of a positive view of ICT.
Strategy Focus/Outcome Staff awareness and change of negative attitudes and behaviour
• Provide awareness of negative attitudes and behaviour
Student awareness and change of negative attitudes and behaviour
• Provide awareness of negative attitudes and behaviour
• Provide skills to counteract negative attitudes and behaviour
Examples of real-world benefits • Provide direction and an end goal
Provide role models • Provide incentive to continue
Provide mentors • Provide support to continue
Offer new or modified courses • Provide appropriate course pathways for students
Women’s Network • Provide opportunities to socialise
• Provide opportunities to lead • Provide additional learning opportunities
Entrance priority to females • Provide incentive to participate
Promote a positive view of ICT via popular media • Provide awareness of future careers
Table 5.7 Strategies suggested by ICT industry mapped to the focus or expected outcome and classified as Social (S), Rational (R) or both (S&R)
The final step in this research is to map these strategies to the factors contributing to attrition
using the socio-rational approach as demonstrated in Table 5.8:
139
Strategy Focus/Outcome Contributory Factor(s) Behaviour and attitude awareness and change
Provide awareness of negative attitudes and behaviour
Didn’t fit in or belong Didn’t make friends Not encouraged by Teachers In minority in class Couldn’t get help Didn’t enjoy classes Academic environment unsuitable
Equip students with awareness of and skills to counteract negative attitudes and behaviour
Provide awareness of negative attitudes and behaviour Provide skills to counteract negative attitudes and behaviour
Didn’t fit in or belong Didn’t make friends Not encouraged by Teachers In minority in class Couldn’t get help Didn’t enjoy classes Academic environment unsuitable
Examples of real-world benefits
Provide direction and an end goal
Course lacked workplace focus Course lacked business focus Course lacked practical applications
Provide role models Provide incentive to continue
Picked wrong degree
Provide mentors Provide support to continue
Picked wrong degree Course lacked workplace focus Course lacked business focus Course lacked practical applications Too many distractions Not encouraged by Teachers
Offer new or modified courses
Provide appropriate course pathways for students
Picked wrong degree Course expectations unmet Didn’t understand concepts Didn’t understand terms Teaching pace too fast Teachers didn’t explain exercises Results not as expected Course too theoretical Course lacked workplace focus Course lacked business focus Course lacked practical applications Course too mathematical Didn’t enjoy classes Boring classes Academic environment unsuitable I lacked expected knowledge
Women’s network Provide opportunities to socialise Provide opportunities to lead Provide additional learning opportunities
No or few females in class Didn’t make friends Didn’t feel I fit in or belonged Course lacked workplace focus Course lacked business focus Course lacked practical applications Couldn’t get help In minority in class
Entrance priority to females Provide incentive to participate
No or few females in class Didn’t feel I fit in or belonged
Promote a positive view of ICT via popular media
Provide awareness of future careers
Picked wrong degree
Table 5.8 Strategies suggested by ICT industry mapped to attrition contributory factors
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Interestingly, the industry member’s suggested strategies can be mapped to a larger number
of the contributory factors than those made by the members of ACDICT. The recognition of
such issues as those caused by the negative attitudes and behaviour of peers and academics,
and the need to modify or offer more suitable courses, allows more than half of the
contributory factors to be addressed.
5.2 Synthesis of Student, ACDICT and ICT Industry Data
Notwithstanding the earlier criticisms of the strategies suggested by both ACDICT and
members of industry, Table 5.9 demonstrates that, together, the proposed remedies address
almost 100% of the factors identified by ex-ICT students as significant factors contributing to
attrition:
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Strategy Source Focus/Outcomes Contributory Factors Lit ACD ACS
Women’s network Provide opportunities to socialise Provide opportunities to lead Provide additional learning opportunities
No or few females in class Didn’t make friends Didn’t feel I fit in or belonged Course lacked workplace focus Course lacked business focus Course lacked practical applications In minority in class Couldn’t get help
New or modified course Provide appropriate course pathways for students
Picked wrong degree Course expectations unmet Didn’t understand concepts Didn’t understand terms Teaching pace too fast Teachers didn’t explain exercises Results not as expected Course too theoretical Course lacked workplace focus Course lacked business focus Course lacked practical applications Course too mathematical Didn’t enjoy classes Boring classes Academic environment unsuitable I lacked expected knowledge
Examples of real-world benefits
Provide direction and an end goal Course lacked workplace focus Course lacked business focus Course lacked practical applications
Small group learning
Promote integration by providing social contact Promote integration by counteracting fragmented experience for non-traditional students Promote integration by counteracting fragmented experience for mature-age students Promote progression of students Promote retention of students Foster student potential
Didn’t feel I fit in or belonged No or few females in classes Didn’t make friends Teaching pace too fast Teachers didn’t explain exercises Didn’t understand concepts Didn’t understand terms Course too theoretical
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Promote integration by providing valuable skills to traditional students
Course too mathematical Results not as expected Didn’t enjoy classes Boring classes In minority in class Not encouraged by Teachers Too many distractions
Relationship building with cohort by administration
Demonstrate institutional commitment Personalise the institution Foster institutional affiliation Ease transition Identify problems/potential problems
Didn’t feel I fit in or belonged Results not as expected Couldn’t get help
Modify prerequisites Remove programming prerequisite
Didn’t feel I fit in or belonged No or few females in classes Didn’t make friends In minority in class
Broaden admissions Include leadership as a candidate characteristic Include community contribution as a candidate characteristic
Didn’t feel I fit in or belonged No or few females in classes Didn’t make friends In minority in class
Identify high-risk courses Address causes of high risk Provide resources to counteract high risk Reduce withdrawals
Picked wrong degree Course expectations unmet Teaching pace too fast Didn’t understand concepts Didn’t understand terms Course too theoretical Course too mathematical Didn’t enjoy classes Boring classes Not encouraged by Teachers I lacked expected knowledge In minority in class
Monitor ‘at risk’ students Identify struggling students immediately Reduce student stress Identify course structure issues
Didn’t feel I fit in or belonged No or few females in classes Results not as expected Picked wrong degree Didn’t understand concepts
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Didn’t understand terms Course too mathematical Course too theoretical Couldn’t get help In minority in class Didn’t enjoy classes Boring classes Too many distractions
Recognise educational differences
Provide appropriate course pathways for students
Didn’t feel I fit in or belonged Didn’t understand concepts Didn’t understand terms Teaching pace too fast Teachers didn’t explain exercises Course lacked workplace focus Course lacked business focus Course too theoretical Course too mathematical Didn’t enjoy classes Boring classes Not encouraged by Teachers In minority in class
Behaviour and attitude change
Provide awareness of negative attitudes and behaviour
Didn’t feel I fit in or belonged Didn’t make friends Not encouraged by Teachers In minority in class Couldn’t get help Didn’t enjoy classes Academic environment unsuitable
Teach skills to counter negative attitudes/behaviour
Provide awareness of negative attitudes and behaviour Provide skills to counteract negative attitudes and behaviour
Didn’t feel I fit in or belonged Didn’t make friends Not encouraged by Teachers In minority in class Couldn’t get help Didn’t enjoy classes Academic environment unsuitable
Entrance priority to females Provide incentive to participate No or few females in class Didn’t feel I fit in or belonged
Mentors Provide support to continue Picked wrong degree
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Course lacked workplace focus Course lacked business focus Course lacked practical applications Too many distractions Not encouraged by Teachers
Role models Provide incentive to continue
Picked wrong degree Course lacked workplace focus Course lacked business focus Course lacked practical applications Too many distractions Not encouraged by Teachers
Connect with high schools Improve high school teacher’s skills Improve high school teacher’s knowledge Highlight failings in classroom management
No or few females in classes In minority in class
Promote positive view of ICT via popular media
Provide awareness of future careers Picked wrong degree
Women-specific club Provide opportunities to socialise Provide opportunities to lead Provide additional learning opportunities
No or few females in class Didn’t feel I fit in or belonged Didn’t make friends Course lacked workplace focus Course lacked business focus Course lacked practical applications In minority in class Couldn’t get help
Women-specific events Provide opportunity to socialise Provide direction and end goal Provide incentive to continue
No or few females in class Didn’t feel I fit in or belonged Didn’t make friends Course lacked workplace focus Course lacked business focus Course lacked practical applications In minority in class
Financial incentive Provide incentive to participate Didn’t make friends Uni too expensive In minority in class
Financial help Provide incentive to continue Uni too expensive Conflict with work Timetable conflict with work
Table 5.9 Strategies suggested by the literature (Lit), ACDICT members (ACD) and members of the ICT industry (ACS) mapped to attrition contributory factors
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Although the factors identified as significant contributors to attrition could not be addressed
solely by the strategies suggested by ACDICT, when combined with the broader set of
strategies suggested by the industry members and the literature it is apparent that these issues
could be ameliorated, thus preventing these particular problems from impacting students
either as a single insurmountable issue or in combination with factors outside the control of
the university or the student.
Now that this matrix of factors and strategies has been identified it is possible for universities
to target specific groups of students and know that that they are effectively using strategies
that will address the factors that do affect those groups or to target larger groups of students,
such as all first year students, and pick particular factors that concern them.
5.3 ICT Student Attraction and Retention Initiative
An initiative that has the potential to appeal to all ICT students to provide outreach,
assistance, support and encouragement is one that is modelled, in part, on the successful
program established at Carnegie Mellon University.
The women-friendly ICT Club could also contribute to the learning outcomes for first year
students by putting them in contact with their peers who are further advanced in their ICT
degrees. As with the Women@SCS Advisory Council (Frieze & Blum, 2002), it is likely to
also attract male students and this, in turn, would create a noticeably better and more
supportive environment for all members as it did in the School of Computer Science at
Carnegie Mellon University (Frieze et al., 2006). Although there may be some time and effort
required from staff members – both academic and general – the Club needs to be driven by
the members as much as by the permanent staff of the school because programs created by an
enthusiastic ‘champion’ are frequently ad hoc and can be fragmented (Craig et al., 2007). A
successful Club would encompass many of the strategies suggested by the members of
ACDICT and industry and by the literature by offering opportunities to network, learn about
future careers, and hear from and talk to successful people. These kinds of opportunities will
assist in reducing some of the stress caused for young women in being a minority, while
creating a more collegial atmosphere for all students (Frieze et al., 2006). It is also likely that
some of these students will become future success stories and mentors for those who follow
them (Frieze & Blum, 2002). One of the ways to achieve this would be through a formal
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mentoring system. Providing a way to track students in a non-intrusive manner by creating a
personal connection to an academic or an industry member would allow students to be more
forthcoming in conversation and facilitate discussion of their problems and concerns which
might otherwise remain hidden (Jacobi, 1991). There will, however, still be students who are
struggling. Those students should be identified early – not after their first major exams – and
given all possible assistance, if attrition is to be reduced. This assistance may be in a variety
of forms as the discussion of the suggested strategies demonstrated but, whatever the
solution, a system to track each identified student is needed so that it is possible to know
whether the mentor and the assistance given has had the desired outcome and ensure
alternative solutions are offered.
Not only would the Club serve the interests of currently enrolled students, it would also be
the hub of a variety of outreach activities which could involve current university students
who would be role models for the younger students while also creating a welcoming and
exciting experience to enthuse students about ICT and its many applications in real-world
scenarios. There would also be outreach to school teachers and parents to ensure they were
fully informed about the future careers of students who have graduated with an ICT degree.
Figure 4.2 depicts how the Club could become the heart of the outreach effort, both within
the university to attract students in other disciplines to take complimentary computing
courses, as well as external outreach to schools.
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Figure 5.1 Women-friendly Club diagram depicting outreach and collaboration
The double-headed arrows in Figure 5.1 indicate outreach and collaboration with industry,
undergraduate and postgraduate students and the faculties. The single-headed arrows show
outreach efforts and who is targeted both to assist in the outreach efforts (undergraduate and
postgraduate students) as well as those who are the intended recipients of those efforts
(students, parents, teachers, careers advisors, principals and members of other faculties).
Although the Club’s central raison d’être is to support female students and create a positive
and welcoming space, the diagram shows that it could become a powerful tool for inspiring,
attracting and integrating all students as well as garnering assistance and input from industry.
Of course there may be objections to the instigation of a proactive program that pays far
greater attention to the progress of students and actively involves staff in intervening when
students are struggling, because it may be seen as inappropriate to the culture and structure of
higher education. It also cannot be denied that there may be objections based on the need for
some level of additional funding for this program to succeed (McInnis et al., 2000b). It is,
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however, reasonable to assert that the current passivity of university systems and staff, which
is a direct result of the prevailing culture, is a factor which may contribute to attrition and if
industry, government and higher education institutions are serious about increased retention
in ICT, such measures should not only be considered, as McInnis et al. (2000b) suggested,
but must actually be tried.
5.4 Outcomes of the Capstone Interviews
To satisfy objective five of this research, experts were asked to comment on the identified
contributory factors and validate the socio-rational approach being taken to understand
attrition in ICT degrees. To that end, a number of Deans of ICT were invited to participate in
interviews designed to validate the findings made by this research. Of those invitees four
members – three male and one female – made themselves available, via Skype or face-to-
face, and were asked to give their views on the statements contained in a stripped down
version of Table L27 (see Appendix L). The interviewees were asked five questions to obtain
their thoughts on attrition and their reactions to the content of the table:
Question 1 – What contributory factors come to mind when you think about attrition?
This question allowed each person to gather their thoughts on the subject, based on their
years of experience, and put them in a frame of mind that would allow them to absorb the
information presented whilst also giving the interviewer an understanding of the
interviewee’s current expectations and point of view.
Four themes emerged from the responses to Question 1:
• Social engagement – connecting to new people having lost the network that existed at
high school.
• Academic engagement – learning how to cope with their new circumstances by
understanding how university works.
• Accommodating to a new learning style – failure to do so may result in students being
‘at risk’ of quitting.
• Encountering new teaching practices and attitudes – members of minority groups may
be disadvantaged by inappropriate methods and approaches
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All of these themes have been identified by researchers as potential contributors to attrition.
At the core of this research is Tinto’s (1975) Social Integration Model and his continued
work on this idea (1986, 1993). The themes identified fit this model as the ability of entering
university students to connect with new people and fit into the academic as well as social life
of the university environment are central concepts. Coping with new circumstances by
learning how to navigate the university environment was also of interest to Bray et al. (1999),
Christie et al. (2008) and Beasley and Pearson (1999). Beasley and Pearson (1999, p304)
point out that there is a distinctive culture of knowledge at university and “the rules of the
culture are seldom made explicit.” As socialising into this new culture is gradual, those who
are unable to persist long enough do not achieve this.
Of course, there will be students who are ‘at risk’ of failure due to academic or personal
issues that may be unable to be resolved (Blanc et al., 1983; Clark & Ramsay, 1990). If these
difficulties cannot effectively be removed or reduced it will not be possible for the student to
become or remain engaged with their academic work. This inability to engage could also
result from undertaking a course that does not interest the student, especially if there is added
pressure from their family to succeed in a field they may not otherwise have chosen (Sin,
2009; Sjaastad, 2012; Thering, 2011). Part of acclimatising to university study is the ability to
accept, and work with, the established teaching style. Having the flexibility to accommodate
that teaching style, or the failure to do so, was considered by Christie, Tett, Cree, Hounsell &
McCune (2008), Davidson et al. (1999), Bennett (2003) and Georg (2009) and were
identified as problematic by Sander et al. (2000), Yorke (2000) and, more recently, Tinto
(2012). Although much of the focus was on the practical aspects of teaching and learning it
was also recognised that failures in teaching practice and learning ability would also cause
personal conflict (Margolis & Fisher, 2002; Sander et al., 2000), especially if the student does
not see any evidence that other students are also struggling (Manis et al., 1989; Margolis &
Fisher, 2002). This would be compounded by the fact that the student is in a minority group
in a course where the teaching practices are inappropriate. These experiences, which
contribute to the belief that students do not fit in or belong, are very likely to result in a
student believing that they have taken the wrong course which is frequently cited when
students are asked why they quit (Connolly & Murphy, 2005; Harrison, 2006; McInnis et al.,
2000a).
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This discussion demonstrates that all of the themes identified by the experts can, in fact, be
considered necessary to the social and academic integration of students and are vital aspects
contributing to them feeling that they do fit in or belong in their new environment.
Question 2 – Do you think the factors listed in this table are likely to contribute to attrition?
Each interviewee was presented with a list of all contributory factors, ranked from highest to
lowest as shown in Table 5.10:
Contributory Factors in Ranked Order No or few females in class (S)
Didn’t enjoy classes (S)
Picked wrong degree
Course expectations unmet
Boring classes
Didn’t feel I fit in or belong (S)
Didn’t understand concepts
Course lacked workplace focus
Teaching pace too fast
Teachers didn’t explain exercises
Results not as expected
Academic environment unsuitable
Course lacked practical applications
Course lacked business focus
Too many distractions (S)
I lacked expected knowledge
Didn’t make friends (S)
Course too theoretical
University too expensive
In minority in class (S)
Couldn’t get help
Didn’t understand terms
Conflict with work
Timetable clash with work
Not encouraged by teachers
Course too mathematical
Table 5.10 Significant factors in order of rankings from
highest (75.4%) to lowest (33.3%)
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The Interviewees were asked to state whether the identified factors corresponded with their
expectations, knowledge and experience and all four agreed that they could see that all of the
factors were likely to contribute. Two of the interviewees added to their confirmation by
stating that they were able to see correspondence between their answers to question 1 and the
list of contributory factors.
Question 3 – Are you surprised by the ordering of the responses?
The statements in Table 5.10 were ranked from the highest amount of agreement (agree and
strongly agree rankings added together) they had received (75.4%) to the lowest (the cut off
point of 33.3%). All of the interviewees remarked on the highest ranked statement with two
pondering whether the reasoning for this was that students were aware of the paucity of
female students, rather that it being a factor contributing to their decision to quit. One
interviewee remarked that it made sense as “at my university last year there was only one
female in a course which enrolled 52 students.” Two interviewees also commented on the
ranking of statements. One was surprised that “Uni too expensive” was identified as a factor
by more than one third of students while another commented that they would have expected
that “issues about the relevance of course content” were not higher on the list.
It is clear that all the interviewees found the statement about the lack of female students being
the highest rated contributory factor problematical. There may be some validity in their
suggestion that the questionnaire respondents were agreeing with this statement because it
was a fact, rather than a factor in their decision to quit. For the female respondents, however,
it remains likely that this was a factor in their decision to quit as being the only or one of a
few females in a class would be daunting when coupled with other issues such as not making
friends with classmates, and finding the course was not what was expected.
Question 4 – Is there anything you are particularly surprised is not on this list?
The interviewees all indicated that they believed that the list was comprehensive and that they
were unable to identify anything obvious that was missing though two did note that it was
surprising that there were “not more personal external reasons” and “not more
acknowledgement that the fault lay with the student” but reasoned that “we tend to want to
blame outside factors”.
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It is interesting to note that although the list contained only 19% of all the factors identified in
the literature (26 out of 140), respondents were not surprised that 81% of possible factors
were missing. Presumably the omitted factors did not strike them as being high frequency
issues they would have expected to see on the list. This implies that the list of factors
contributing to attrition is reasonably comprehensive.
Question 5 – Do you think it is useful to include both social and rational factors in explaining
attrition?
Before concluding the interview, the socio-rational approach used in the research was briefly
explained and this allowed each interviewee to understand why “(S)” had been placed beside
some statements to indicate those that were classified as social integration factors
contributing to attrition. It also made it clear that the unmarked items were factors of a
rational nature with the example given of a student finding conflicts between the university
timetable and their work commitments. All four interviewees believed that the approach was
valid with one interviewee stating that they believed it would “allow us to have greater
success in identifying issues and potentially addressing them” while another remarked that it
“covers a spectrum of reasons for students quitting and provides a rationale that seems to
fit”.
Although some interviewees questioned one or two individual aspects, in general all four
believed the socio-rational approach was valid and made a contribution to the body of
knowledge about attrition from ICT degrees. They believed that the socio-rational approach
had in fact identified a reasonably comprehensive set of factors that may lead to attrition from
ICT degrees and the factors they had identified at the beginning of their interviews were
substantiated by the literature as well as the findings of this research.
5.5 Conclusion
In this chapter relevant data from questionnaires completed by members of ACDICT and a
survey of people working in the ICT industry were extracted including responses to questions
about strategies the respondents could suggest to overcome the problems associated with
attracting and retaining students in ICT degrees. These strategies, together with those
identified in the literature, were discussed and a scheme to determine whether each strategy
could be classified as a social or rational solution was established.
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Having determined the classification of the suggested strategies it was then possible to
introduce the ICT Student Attraction and Retention Initiative which combines a number of
individual strategies into a holistic approach involving:
• Establishing a women-friendly Club
• Outreach to primary schools (inviting them to participate in events)
• Outreach to high schools (inviting them to participate in events)
• Input from industry (encouraging them to sponsor and participate in events)
• Input from the university, faculty and schools (organisation and participation in events
and club)
• Monitoring of student progress
• Mentoring of students
The chapter ended with the validation of the socio-rational approach by four members of
ACDICT who were asked: to consider the contributory factors that came to mind when they
thought about attrition; whether the 26 listed factors listed were likely to contribute to
attrition; whether they were surprised by the ordering of the responses or surprised that a
factor was not on this list and; finally, whether they believed it was useful to include both
social and rational factors in explaining attrition.
Although the ACDICT members did not agree with some of the details, overall they believed
the socio-rational approach to identifying the factors contributing to attrition in ICT degrees
was valid and would contribute to a greater understanding of the reasons students quit their
studies.
The final chapter presents the conclusions drawn from the data and its analysis by:
identifying factors in students’ experiences of their university’s environment, their ICT
courses and their personal life that may have contributed to attrition; revealing strategies used
to reduce student attrition and; finally, mapping strategies to the contributory factors
identified by students in order to validate the socio-rational approach. The implications of
these findings will be presented, together with recommendations for action in the final
chapter.
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6 CONCLUSION
6.1 Introduction
As stated at the beginning of this thesis, student attrition is an issue of serious concern to
universities around the world. It is of particular concern to the field of ICT because of the
shortfall of ICT professionals (ACS, 2008, 2011; ITU, 2012). The current study has taken a
socio-rational approach to attrition in order to:
i) identify the main factors which ex-ICT students indicated had contributed to their
decision to quit their ICT degrees i.e. contributory factors;
ii) identify strategies that may reduce attrition;
iii) and map appropriate strategies to the most significant contributory factors.
The mapping of factors to strategies resulted in the formulation of a program which would
apply a number of previously used strategies in a holistic manner to combat many of the
factors which have contributed to attrition.
6.2 Research Question and Objectives
The research began with the formulation of the question: “Can a socio- rational approach be
used to identify the factors leading to attrition in Australian ICT degrees and to identify
appropriate strategies to address these factors?” To answer this question the following
objectives needed to be achieved:
1. to produce a comprehensive instrument, based on the socio-rational approach and
specific to ICT degrees;
2. to use this instrument in Australia, where the level of attrition is comparable to much
of the western world, to gather data from students that identifies the factors
contributing to them quitting their ICT degree;
3. to gather data from the literature and experts in academia and industry about strategies
for dealing with attrition, and analyse it using the socio-rational approach;
4. to map the factors contributing to attrition to the strategies identified;
5. to validate the effectiveness of the socio-rational approach by interviewing experts
from academia;
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6. to provide guidelines to reduce attrition by presenting a holistic strategy.
The previous chapters of this thesis met those objectives in the following ways:
Objective 1 was met by the literature review in Chapter 2 which established the difficulty in
defining “ICT” and what an ICT degree comprises through a brief recounting of the history
of computing. It then examined the study of attrition in universities, based on a range of
student characteristics, before discussing the study of attrition specifically in ICT degrees
both in Australia and overseas. The lack of women studying ICT at universities, and their
attrition from ICT degrees, was also briefly examined. This was followed by an investigation
of the strategies suggested and used around the world to combat attrition. During the course
of this chapter Objective 1 was met by the gathering of information from the literature that
assisted in the creation of a comprehensive instrument to survey ex-ICT students. The chapter
concluded with a detailed explanation of the socio-rational approach used to investigate
attrition.
Objective 2 was met in part in Chapter 3 because it described a number of processes that have
been carried out. One of these was the use of a comprehensive instrument to gather data from
ex-ICT students of four Australian universities using an online questionnaire. The other
processes were the individual interviews of a number of the ex-ICT students who had
completed the questionnaire. This chapter also explained the paradigm and context of this
research before describing the methods to be used to gather and analyse the data gathered
from three distinct groups: ex-ICT students; senior ICT academics (referred to as “members
of ACDICT”, and members of the ICT industry. This was followed by an extended
discussion about how the six objectives of the research would be met before concluding with
consideration of the ethics involved in this investigation.
Objective 3 was met partly by the literature review in Chapter 2 and also by the members of
ACDICT and the ICT industry completing a questionnaire in which they identified strategies
that had been used to combat attrition. These strategies were analysed in Chapter 5 using the
socio-rational approach.
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Objectives 4, 5 and 6 were also met in Chapter 5. Objective 4 was achieved by mapping the
26 identified significant contributory factors to the 21 strategies suggested by the members of
ACDICT and the ACS, or identified in the literature, using the socio-rational approach. This
analysis demonstrated that any given strategy could counter more than one of the factors
contributing to attrition thus using a number of strategies together would be likely to reduce
attrition not only for the entire cohort but even more importantly would address difficulties
encountered by significant groups within that population.
Objective 5 was met by conducting capstone interviews with four members of ACDICT to
determine whether they regarded the findings of this research as valid. The interviews also
garnered the support of these academic experts for the socio-rational approach undertaken in
this research. Each expert believed this approach would contribute to gaining a more
comprehensive understanding of the factors contributing to attrition in ICT degrees in
Australia.
The final objective, Objective 6, was accomplished by synthesising all the gathered data and
formulating the “ICT Student Attraction and Retention Initiative”. This initiative combines
the identified strategies in a holistic program which would address the contributory factors to
attrition for significant groups within the student cohort as well as benefitting all students
participating in the outreach activities of the women-friendly Club. Although the Club’s
mission is to support female students and create a positive and welcoming space, it could also
be a powerful tool for integrating, attracting and inspiring all students as well as becoming a
centre of attention for industry.
6.3 Research Findings
This research was undertaken with the expectation that the rate of attrition in ICT degrees in
Australia would be at a similar concerning level that it is in other parts of the western
industrialised world. The statistics supplied by the Australian government showed that both
women and men have been enrolling in ICT degrees in decreasing numbers while the attrition
rate, as a percentage of commencing students, has remained at approximately one-sixth
during the 8 years over which the statistics were gathered.
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Central to this study are the works of Tinto and Bean whose models had been used almost
entirely in parallel and had rarely been combined before. Their models of student social
integration (Tinto, 1975) and rational decision making (Bean, 1983) were combined in this
study and their core ideas applied to attrition in ICT degrees in Australia. This approach was
taken after considering the works of Cabrera, Nora and Castaneda (1993) and Weng, Cheong
and Cheong (2010). Those authors had attempted to combine Tinto’s (1975, 1987) and
Bean’s (1980, 1985) models with varying degrees of success and accuracy. This study has
extended the work of all these researchers to produce a relatively comprehensive list of
factors contributing to attrition as well as identifying strategies to mitigate them using the
socio-rational approach.
This study not only identified a rich set of factors which had been found to contribute to
attrition by previous research but it also confirmed that these factors were significant for the
ex-ICT students who participated in the questionnaire. The dominant group who responded to
this questionnaire was male domestic full-time students and 12 factors were identified which
had contributed significantly to their decision to quit. Notwithstanding the dominance of this
group, an additional 14 factors were identified as having a significant impact on several sub-
groups of ex-ICT students. In fact the responses to the questionnaire revealed that for many
students it was a smaller set of the identified factors that caused difficulties for them. Not
only was the set of identified contributory factors smaller but it was a combination of some of
these factors which had led students to quit.
The questionnaire data in this study also revealed that trends identified in previous research
did not occur. Concerns that might have had significance for female students particularly
such as pregnancy or caring responsibilities for young or older family members were not
statistically significant. The most significant factor for male students was that there had been
too many distractions preventing them from concentrating on their studies. This contradicts
the assumption that men learning about ICT are focussed on their studies to the point of being
myopic.
Full-time students were significantly affected by boring classes that were too theoretical and
too mathematical while a significant number of traditional age students agreed that they did
not understand the concepts being used in their course and that it was too theoretical and
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mathematical. As the literature and those teaching ICT assume that students enter ICT
degrees with an already-acquired prior level of knowledge and ability, this was unexpected
and revealed a need to consider whether the current pathways are appropriate for the
significant groups within the cohort enrolling in ICT degrees in Australia.
Despite the fact that much of the literature claims that high school ranking is an accurate
indicator of university performance and persistence the majority of both male and female ex-
ICT students indicated that they had been in the top 30% of their cohort when they left high
school. This is an important finding and, when combined with the significant number of ex-
ICT students who agreed that classes were boring, it suggests that high-achieving students
may have been unchallenged by their ICT courses. Contrary to expectation this suggests that
the more capable, rather than the less able students, are most at risk of attrition.
Since the questionnaire is the compilation of the reasons for attrition found in the literature, it
is clear that some of the ex-ICT students were influenced by the factors identified in that
review. Several of those factors are not given the emphasis in the literature that they deserve.
Despite the evidence that there has been a move away from students being able to focus on
full-time study with many needing to work as well as study, this has only recently been
acknowledged. It is also apparent that the teaching of ICT is not without its problems, such as
the assumptions made about the skills and knowledge students have already acquired before
undertaking an ICT degree. Both the teaching practices and the courses offered need closer
examination, as neither has been recognised as problematical by most researchers in the past,
but have been revealed in this study as areas of concern.
Notwithstanding the fact that the questionnaire presented a relatively comprehensive set of
statements for the ex-ICT students to rate and gathered a number of data sets on the
demographic characteristics of the respondents, 16 additional factors were suggested in the
written responses to the open-ended questions, however each of these factors was only
identified by one or two respondents, they are subjective and cannot be substantiated. These
factors were also issues for individuals and related to problems that were of a personal nature,
so the sub-set of contributory factors identified by this study appears to be relatively
comprehensive. The 26 factors identified by a significant number of the respondents were
subsequently classified as pertaining to the social integration of students, rational decision-
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making by students, or a combination of social integration and rational decision-making,
using the socio-rational approach.
Although much research found in the literature ends by presenting findings such as these, this
study began with the intent to extend that research by identifying strategies – a topic that has
been widely overlooked – in the literature and enriching those findings by adding the
strategies identified by the members of ACDICT and the ICT industry. These strategies were
then also classified as pertaining to the social integration of students, rational decision-
making by students, or a combination of social integration and rational decision-making,
using the socio-rational approach.
Having identified and classified both the most significant factors contributing to attrition and
the strategies that were likely to combat them, it was then possible to map them to one
another. Once the strategies and contributory factors had been mapped it was possible to
conceive of a program that would take advantage of these findings by combining a number of
initiatives into a holistic program aimed at mitigating many of the most significant factors
contributing to attrition. This program would aid in the integration and retention of students
by providing them with a social hub where they can make friends, participate in educational
events and accept leadership opportunities while also having the potential to attract a greater
diversity and larger number of students into a field which is increasingly in need of qualified
graduates.
Capstone interviews with 4 experts demonstrated the overall validity of the socio-rational
approach which they were willing to ratify.
6.4 Significance
There are a number of groups for whom the current research is significant: researchers;
university administrators, Deans and Heads of Schools of ICT; the ICT industry; and
potential, current and future ICT students.
Researchers in the field of attrition could make further use of the socio-rational approach by
applying it to ICT or to other fields of education or other educational institutions such as
TAFE. They also now know that some of the expectations about the issues affecting women
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and assumptions in the literature and in teaching ICT that all students have some prior
knowledge and experience of ICT should be questioned. Researchers could make use of the
comprehensive list of the contributory factors presented in this study and apply the strategies
identified to ameliorate them or consider the efficacy of implementing of the “ICT Student
Attraction and Retention Initiative” presented in this study.
University administrators and Deans and Heads of School of ICT could adopt the “ICT
Student Attraction and Retention Initiative” to reduce the likelihood that the identified
contributory factors will result in attrition of their students. Importantly, they can now
identify particular groups within the student cohort which need particular support and, by
using the mapping of factors and strategies, identify a set of targeted strategies to assist each
group in more specific ways. The initiative would not only assist in retaining current students
but would also assist in attracting potential students who may not have considered attaining
an ICT degree, as well as confirming the desirability of an ICT degree to future students
already interested in continuing their study in the field.
The initiative could not only benefit universities and their current and future students but
could also assist the ICT industry which needs an increasingly larger number of graduates.
Importantly, though, those graduates could be from the spectrum of humanity, whether that is
more female graduates or graduates from a variety of cultural and economic backgrounds,
rather than just a greater number of men. The potential inputs from a graduate body which
encompasses gender, economic and cultural diversity in a more representative fashion would
also be diverse, richer and likely to take ICT in directions that might otherwise not be
conceived. The more diverse the ICT workforce becomes, the easier it will be to attract
greater diversity and this, in itself, could address some of the perceptions currently held by
the community about the suitability and sustainability of a career in ICT. Industry’s greater
involvement with universities and ICT students, as advocated by the initiative, would
demonstrate its commitment to the education and well-being of its future ICT employees.
Participation in and advocacy of the initiative by industry would give greater credence to it
for parents, school teachers and counsellors as well as current, potential and future students.
It is also conceivable that the ICT industry peak bodies could lobby government to address
some of the factors or could themselves adopt strategies identified in this study to ameliorate
161
some of the problems that almost certainly lie outside the influence of individual universities
or ICT faculties.
Potential, current and future ICT students could also benefit from the findings of this study if
their university adopts strategies to address the significant contributory factors affecting
them. If their university chooses to implement the “ICT Student Attraction and Retention
Initiative” they will become part of the holistic strategy and contribute to its and their own
success. Providing opportunities for students to participate in activities which allow them to
give and to gain a richer and more inclusive experience of ICT in a welcoming environment
will enhance their current experience of the field, confirming that they have made the right
choice to work towards becoming an ICT graduate whether they are in primary school, high
school or already enrolled in an ICT degree.
6.5 Limitations
There were deliberately imposed limitations on this study because there must be a certain
scope to any research project in order to ensure it can be achieved. The limitations were as
follows:
• data gathered from the three participating groups: ex-ICT students; members of
ACDICT; and members of the ACS were from questionnaires and each was a one-off
study rather than longitudinal data gathering exercises;
• this study was limited to Australian universities however, given that the incidence of
attrition in Australian degrees is similar to that overseas, it might be assumed that the
results of the current research could be applicable to universities overseas. It is
possible, of course, that some factors or strategies identified in this study may not
contribute as greatly to attrition overseas;
• the students belonged to only four of the 37 public universities however, each is
identified by the Australian government as belonging to different categories of
universities: Group of 8 (QUT); Technology (SUT); Metropolitan (UOW); and
Innovative Research Universities (MU). They also vary in student cohort size,
geographical location and proximity to capital cities in Australia. Nevertheless, it
could be argued that the factors leading to attrition in elite universities belonging to
the Group of 8 or small rural universities are quite different to the ones indentified in
this study. Regardless of these considerations, students will, in most respects, be
162
similar enough that it is possible to be reasonably confident that the results in this
study would be applicable.
• this study did not consider all of the factors (i.e. a variety of psychological
characteristics which could affect the decision-making process) that have been
identified by researchers in their attempts to understand the motivations of students
who decide to quit.
• this study was limited to university education so some of the factors or strategies
identified may not be applicable to other forms of higher education such as TAFE or
professional development. It is likely, however, that most of the factors and strategies
would be relevant and that a socio-rational approach is valid for these other types of
education though this study cannot make claims about attrition in those styles of
education.
6.6 Future Directions
As there were deliberately imposed limitations on this study the future directions that could
be taken are as follows:
• an empirical investigation of attrition distinguishing between loss of students from
higher education as well as from ICT degrees could be conducted to determine
whether students were leaving the ICT discipline or the institution;
• a longitudinal study could be undertaken using the socio-rational approach to gather
data from a larger number of ex-ICT students from a greater number of universities
over time. This would allow trends to be identified which would assist in refining the
holistic program proposed here;
• the socio-rational approach could also be applied in other disciplines as the principles
underpinning it allow the approach to be used to understand attrition in any field of
study;
• research using the socio-rational approach could be conducted on universities outside
Australia to determine its efficacy in confirming the contributory factors identified or
to discover different factors affecting students in other parts of the world, especially if
this approach was applied in other parts of the western industrialised world and also in
non-western industrialised and less-industrialised countries;
163
• students in elite or rural universities could be the subject of other studies using the
socio-rational approach to determine whether the factors contributing to their attrition
were similar or vastly different to the ones identified in this study;
• research could be conducted in both the TAFE and professional education sectors to
ascertain whether there were differences in the strength or mix of the social and
rational contributory factors when compared to the findings of this study.
6.7 Conclusion
The research presented here has answered the proposed question: “Can a socio-rational
approach be used to identify the factors leading to attrition in Australian ICT degrees and to
identify appropriate strategies to address these factors?” with the creation of a unique means
by which to understand attrition: that of the socio-rational approach. This approach
recognises that a students’ ability to integrate into both the social and academic aspects of
university life can contribute to their decision to quit but that they are also able to make
rational decisions when determining what they believe to be the right course of action.
Recognising these two aspects of students: the social and the rational, by bringing together
the models of Tinto (1975) and Bean (1980), enables a richer understanding of their
motivations to be achieved and has allowed this study to identify: 26 factors contributing
significantly to attrition in ICT degrees; and 21 strategies to combat those factors. By using
the socio-rational approach it has been possible to also classify and map the identified factors
to the strategies.
This mapping process has not only demonstrated that many of the contributory factors can be
addressed by each of the identified strategies but has also culminated in the formulation of a
potential solution to reduce attrition by the introduction of a holistic program. This program
has both the potential to assist current ICT students to integrate into the social and academic
lives of their university, thus reducing attrition from ICT degrees, and also to attract a larger
and more diverse group of future students. Both the increase in numbers of women and men,
and a wider diversity of both genders graduating with ICT degrees, will secure an ongoing
supply of qualified entrants into the ICT industry ensuring its continued viability as a result
of the input of a wider range of people.
164
As a result of these various achievements, this study has made a significant contribution to
the research conducted on attrition in both higher education and in ICT degrees and in doing
so has achieved all six goals of this research.
165
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181
APPENDIX A: GOVERNMENT (DEEWR) DATA ON STUDENT ATTRITION IN ICT DEGREES
Students, Selected Higher Education Statistics (DEEWR)Table: Attrition Rates of Commencing Students in Information Technology by State Author: Saroj Godara 17 Mar 2011RFI 10-324 Roberts
2001 2002 200323896 23560 21220
Charles Sturt University 878 831 942Macquarie University 524 443 459Southern Cross University 238 227 257The University of New England 206 135 106
The University of New South Wales
858 628 418
The University of Newcastle 396 355 278The University of Sydney 299 478 446University of Technology, Sydney 727 759 796
University of Western Sydney 841 829 776University of Wollongong 993 1225 1021Sub-total 5960 5910 5499Deakin University 498 629 733La Trobe University 357 450 466Monash University 2500 2443 2040RMIT University 1564 1329 1405Swinburne University of Technology
1066 949 924
The University of Melbourne 525 447 397University of Ballarat 422 447 692Victoria University 823 983 804Sub-total 7755 7677 7461Central Queensland University 2407 2275 1354
Griffith University 612 653 527James Cook University 179 125 132Queensland University of Technology
1489 1492 1201
The University of Queensland 385 366 395University of Southern Queensland
721 756 671
University of the Sunshine Coast 59 34 45
Sub-total 5852 5701 4325
Males
TOTALNew South Wales
Victoria
Queensland
State/Institution - Attrition rates
e, Institution and Gender, 2001-2008
2004 2005 2006 2007 2008 2001 2002 2003 2004 2005 200620788 16871 14674 14073 14623 4199 4212 3644 3419 2877 2626
782 564 520 498 550 190 224 206 176 131 114344 377 282 244 245 114 87 59 53 50 41162 118 145 142 157 86 99 83 53 42 37324 165 128 106 79 64 50 28 49 30 20
338 361 352 333 310 92 64 39 44 39 49
332 347 440 394 405 60 61 65 53 45 73288 249 271 324 321 35 57 114 39 29 30781 734 523 593 678 132 120 95 88 88 70
474 493 317 242 254 177 135 146 99 89 50762 878 784 1000 1010 98 123 196 96 120 160
4587 4286 3762 3876 4009 1048 1020 1031 750 663 644656 568 472 466 464 80 120 96 99 94 62446 318 310 270 242 65 93 87 81 52 51
1905 1263 1049 890 853 301 305 228 241 152 1411372 1229 1258 1121 1196 346 233 229 282 223 272
677 562 738 848 901 233 161 145 129 94 95
321 286 269 224 143 32 39 34 29 28 251584 827 667 862 907 100 70 125 159 146 124
736 517 368 307 352 199 215 151 158 113 947697 5570 5131 4988 5058 1356 1236 1095 1178 902 8641922 1667 1036 620 754 318 435 258 246 321 249
500 409 316 299 362 101 145 112 128 93 61156 243 306 248 330 46 27 37 48 70 57974 775 644 795 695 189 186 127 138 117 90
367 283 269 169 187 51 62 53 58 40 48801 355 375 381 303 200 254 163 172 97 81
40 41 40 40 33 17 13 19 12 15 12
4760 3773 2986 2552 2664 922 1122 769 802 753 598
ATTRITEDStudent
2007 2008 2001 2002 2003 2004 2005 2006 2007 20082619 2441 17.57 17.88 17.17 16.45 17.05 17.9 18.61 16.69
129 101 21.64 26.96 21.87 22.51 23.23 21.92 25.9 18.3641 39 21.76 19.64 12.85 15.41 13.26 14.54 16.8 15.9241 36 36.13 43.61 32.3 32.72 35.59 25.52 28.87 22.9325 27 31.07 37.04 26.42 15.12 18.18 15.63 23.58 34.18
47 44 10.72 10.19 9.33 13.02 10.8 13.92 14.11 14.19
67 52 15.15 17.18 23.38 15.96 12.97 16.59 17.01 12.8429 33 11.71 11.92 25.56 13.54 11.65 11.07 8.95 10.2876 54 18.16 15.81 11.93 11.27 11.99 13.38 12.82 7.96
54 44 21.05 16.28 18.81 20.89 18.05 15.77 22.31 17.32129 97 9.87 10.04 19.2 12.6 13.67 20.41 12.9 9.6638 527 17.58 17.26 18.75 16.35 15.47 17.12 16.46 13.15
75 97 16.06 19.08 13.1 15.09 16.55 13.14 16.09 20.9165 52 18.21 20.67 18.67 18.16 16.35 16.45 24.07 21.49
124 96 12.04 12.48 11.18 12.65 12.03 13.44 13.93 11.25189 194 22.12 17.53 16.3 20.55 18.14 21.62 16.86 16.22152 143 21.86 16.97 15.69 19.05 16.73 12.87 17.92 15.87
22 6 6.1 8.72 8.56 9.03 9.79 9.29 9.82 4.2211 180 23.7 15.66 18.06 10.04 17.65 18.59 24.48 19.85
67 72 24.18 21.87 18.78 21.47 21.86 25.54 21.82 20.45905 840 17.49 16.1 14.68 15.3 16.19 16.84 18.14 16.61198 167 13.21 19.12 19.05 12.8 19.26 24.03 31.94 22.15
64 79 16.5 22.21 21.25 25.6 22.74 19.3 21.4 21.8245 72 25.7 21.6 28.03 30.77 28.81 18.63 18.15 21.82
149 105 12.69 12.47 10.57 14.17 15.1 13.98 18.74 15.11
35 32 13.25 16.94 13.42 15.8 14.13 17.84 20.71 17.1167 53 27.74 33.6 24.29 21.47 27.32 21.6 17.59 17.49
10 12 28.81 38.24 42.22 30 36.59 30 25 36.36
568 520 15.76 19.68 17.78 16.85 19.96 20.03 22.26 19.52
Attrition rate
Curtin University of Technology 569 536 457
Edith Cowan University 726 831 736Murdoch University 307 343 223The University of Western Australia
208 264 212
Sub-total 1810 1974 1628The Flinders University of South Australia
139 178 146
The University of Adelaide 90 163 163University of South Australia 951 654 616Sub-total 1180 995 925University of Tasmania 394 394 576Sub-total 394 394 576Charles Darwin University 129 98 79Sub-total 129 98 79The Australian National University
159 157 153
University of Canberra 404 355 346Sub-total 563 512 499Australian Catholic University 253 299 228Sub-total 253 299 228
23896 23560 21220Total
Western Australia
South Australia
Tasmania
Northern Territory
Australian Capital Territory
Multi-State
456 429 289 285 273 135 132 105 88 111 64
658 706 580 614 586 141 152 154 121 145 114194 185 192 210 232 45 67 46 40 38 29167 122 97 105 124 26 33 22 27 23 10
1475 1442 1158 1214 1215 347 384 327 276 317 217138 88 72 58 71 38 40 38 27 12 8
174 177 175 170 171 23 50 42 23 22 17635 612 523 370 276 204 112 122 118 78 97947 877 770 598 518 265 202 202 168 112 122725 452 462 389 636 57 76 64 125 36 89725 452 462 389 636 57 76 64 125 36 89
36 31 44 53 38 49 35 29 10 11 1936 31 44 53 38 49 35 29 10 11 19
145 125 103 135 162 20 27 19 27 16 17
222 180 163 177 226 74 45 54 37 30 36367 305 266 312 388 94 72 73 64 46 53194 135 95 91 97 61 65 54 46 37 20194 135 95 91 97 61 65 54 46 37 20
20788 16871 14674 14073 14623 4199 4212 3644 3419 2877 2626
59 60 23.73 24.63 22.98 19.3 25.87 22.15 20.7 21.98
130 110 19.42 18.29 20.92 18.39 20.54 19.66 21.17 18.7745 55 14.66 19.53 20.63 20.62 20.54 15.1 21.43 23.7118 23 12.5 12.5 10.38 16.17 18.85 10.31 17.14 18.55
252 248 19.17 19.45 20.09 18.71 21.98 18.74 20.76 20.4113 17 27.34 22.47 26.03 19.57 13.64 11.11 22.41 23.94
32 29 25.56 30.67 25.77 13.22 12.43 9.71 18.82 16.9674 44 21.45 17.13 19.81 18.58 12.75 18.55 20 15.94
119 90 22.46 20.3 21.84 17.74 12.77 15.84 19.9 17.3757 115 14.47 19.29 11.11 17.24 7.96 19.26 14.65 18.0857 115 14.47 19.29 11.11 17.24 7.96 19.26 14.65 18.0819 12 37.98 35.71 36.71 27.78 35.48 43.18 35.85 31.5819 12 37.98 35.71 36.71 27.78 35.48 43.18 35.85 31.5812 19 12.58 17.2 12.42 18.62 12.8 16.5 8.89 11.73
32 47 18.32 12.68 15.61 16.67 16.67 22.09 18.08 20.844 66 16.7 14.06 14.63 17.44 15.08 19.92 14.1 17.0117 23 24.11 21.74 23.68 23.71 27.41 21.05 18.68 23.7117 23 24.11 21.74 23.68 23.71 27.41 21.05 18.68 23.71
2619 2441 17.57 17.88 17.17 16.45 17.05 17.9 18.61 16.69
2001 2002 20038548 7815 6183
Charles Sturt University 313 226 181Macquarie University 232 165 146Southern Cross University 68 100 67The University of New England 57 39 21
The University of New South Wales
305 181 115
The University of Newcastle 94 84 67The University of Sydney 146 179 142University of Technology, Sydney 306 297 229
University of Western Sydney 228 230 204University of Wollongong 408 411 312Sub-total 2157 1912 1484Deakin University 184 219 192La Trobe University 94 98 80Monash University 1318 1179 882RMIT University 677 503 442Swinburne University of Technology
424 321 229
The University of Melbourne 293 225 175University of Ballarat 88 122 83Victoria University 225 210 229Sub-total 3303 2877 2312Central Queensland University 529 642 353
Griffith University 164 170 96James Cook University 53 54 43Queensland University of Technology
537 472 369
The University of Queensland 149 116 108University of Southern Queensland
227 255 196
University of the Sunshine Coast 14 13 16
Sub-total 1673 1722 1181Curtin University of Technology 252 191 120
Edith Cowan University 204 237 217Murdoch University 83 76 66
Females
TOTALNew South Wales
Victoria
Queensland
Western Australia
State/Institution - Attrition rates
2004 2005 2006 2007 2008 2001 2002 2003 2004 2005 20065099 4144 3466 3347 3876 1480 1334 1034 858 665 584
158 122 71 85 107 65 60 52 38 18 11123 126 83 70 70 42 30 26 15 16 9
37 18 25 34 58 28 48 19 7 5 533 35 25 16 21 22 14 9 6 7 3
79 60 60 57 64 36 24 17 9 6 7
81 77 110 82 122 17 16 10 14 8 1479 64 72 100 80 21 28 33 11 2 7
209 187 177 123 168 66 40 34 34 28 29
80 182 59 35 45 41 38 37 17 20 12209 235 184 202 217 36 46 50 41 25 37
1088 1106 866 804 952 374 344 287 192 135 134121 137 90 71 94 43 37 24 17 18 20
63 49 50 38 41 13 15 12 7 10 8658 391 345 261 251 152 139 102 74 49 38420 311 352 290 330 160 104 77 85 44 89148 133 149 160 174 100 55 33 23 33 17
112 65 63 66 48 11 11 14 5 5 9153 112 82 171 241 16 17 20 25 16 17179 134 112 76 91 55 48 52 27 21 23
1854 1332 1243 1133 1270 550 426 334 263 196 221321 298 223 208 248 97 103 86 49 67 63
114 81 40 51 62 22 31 19 21 22 747 34 97 90 107 13 24 8 12 8 12
254 221 166 201 213 74 67 40 42 42 23
104 80 68 50 51 19 15 11 22 7 10156 101 88 107 58 46 69 41 42 25 11
14 6 2 4 5 0 5 8 5 2 1
1010 821 684 711 744 271 314 213 193 173 127148 187 83 104 85 43 52 30 16 59 13
145 122 107 93 115 38 48 51 34 25 2030 38 35 32 46 18 17 12 8 10 5
Student ATTRITED
2007 2008 2001 2002 2003 2004 2005 2006 2007 2008522 655 17.31 17.07 16.72 16.83 16.05 16.85 15.6 16.9
22 30 20.77 26.55 28.73 24.05 14.75 15.49 25.88 28.0413 9 18.1 18.18 17.81 12.2 12.7 10.84 18.57 12.86
8 9 41.18 48 28.36 18.92 27.78 20 23.53 15.524 2 38.6 35.9 42.86 18.18 20 12 25 9.52
8 7 11.8 13.26 14.78 11.39 10 11.67 14.04 10.94
10 11 18.09 19.05 14.93 17.28 10.39 12.73 12.2 9.028 2 14.38 15.64 23.24 13.92 3.13 9.72 8 2.5
12 19 21.57 13.47 14.85 16.27 14.97 16.38 9.76 11.31
6 8 17.98 16.52 18.14 21.25 10.99 20.34 17.14 17.7825 29 8.82 11.19 16.03 19.62 10.64 20.11 12.38 13.36
116 126 17.34 17.99 19.34 17.65 12.21 15.47 14.43 13.2417 18 23.37 16.89 12.5 14.05 13.14 22.22 23.94 19.15
5 7 13.83 15.31 15 11.11 20.41 16 13.16 17.0733 26 11.53 11.79 11.56 11.25 12.53 11.01 12.64 10.3634 49 23.63 20.68 17.42 20.24 14.15 25.28 11.72 14.8516 28 23.58 17.13 14.41 15.54 24.81 11.41 10 16.09
5 1 3.75 4.89 8 4.46 7.69 14.29 7.58 2.0844 43 18.18 13.93 24.1 16.34 14.29 20.73 25.73 17.8410 18 24.44 22.86 22.71 15.08 15.67 20.54 13.16 19.78
164 190 16.65 14.81 14.45 14.19 14.71 17.78 14.47 14.9667 58 18.34 16.04 24.36 15.26 22.48 28.25 32.21 23.39
12 15 13.41 18.24 19.79 18.42 27.16 17.5 23.53 24.1914 18 24.53 44.44 18.6 25.53 23.53 12.37 15.56 16.8235 44 13.78 14.19 10.84 16.54 19 13.86 17.41 20.66
10 11 12.75 12.93 10.19 21.15 8.75 14.71 20 21.5721 8 20.26 27.06 20.92 26.92 24.75 12.5 19.63 13.79
1 0 0 38.46 50 35.71 33.33 50 25 0
160 154 16.2 18.23 18.04 19.11 21.07 18.57 22.5 20.711 17 17.06 27.23 25 10.81 31.55 15.66 10.58 20
15 22 18.63 20.25 23.5 23.45 20.49 18.69 16.13 19.132 11 21.69 22.37 18.18 26.67 26.32 14.29 6.25 23.91
Attrition rate
The University of Western Australia
61 57 34
Sub-total 600 561 437The Flinders University of South Australia
58 67 42
The University of Adelaide 22 39 32University of South Australia 332 223 236Sub-total 412 329 310University of Tasmania 101 115 274Sub-total 101 115 274Charles Darwin University 46 47 18Sub-total 46 47 18The Australian National University
50 42 30
University of Canberra 146 129 88Sub-total 196 171 118Australian Catholic University 60 81 49Sub-total 60 81 49
8548 7815 6183
Tasmania
Northern Territory
Australian Capital Territory
Multi-State
Total
South Australia
24 25 28 45 21 15 5 4 3 4 5
347 372 253 274 267 114 122 97 61 98 4346 19 21 18 16 10 16 5 10 1 4
39 31 28 31 27 10 7 7 9 2 2184 129 78 88 74 75 39 34 30 24 20269 179 127 137 117 95 62 46 49 27 26361 203 177 168 395 15 15 18 68 9 8361 203 177 168 395 15 15 18 68 9 8
15 9 11 20 11 20 10 6 6 3 315 9 11 20 11 20 10 6 6 3 326 27 28 26 32 2 6 6 7 3 8
59 58 52 54 61 29 23 11 5 13 985 85 80 80 93 31 29 17 12 16 1770 37 25 20 27 10 12 16 14 8 570 37 25 20 27 10 12 16 14 8 5
5099 4144 3466 3347 3876 1480 1334 1034 858 665 584
6 4 24.59 8.77 11.76 12.5 16 17.86 13.33 19.05
34 54 19 21.75 22.2 17.58 26.34 17 12.41 20.223 4 17.24 23.88 11.9 21.74 5.26 19.05 16.67 25
4 7 45.45 17.95 21.88 23.08 6.45 7.14 12.9 25.9316 15 22.59 17.49 14.41 16.3 18.6 25.64 18.18 20.2723 26 23.06 18.84 14.84 18.22 15.08 20.47 16.79 22.22
8 84 14.85 13.04 6.57 18.84 4.43 4.52 4.76 21.278 84 14.85 13.04 6.57 18.84 4.43 4.52 4.76 21.276 4 43.48 21.28 33.33 40 33.33 27.27 30 36.366 4 43.48 21.28 33.33 40 33.33 27.27 30 36.364 4 4 14.29 20 26.92 11.11 28.57 15.38 12.5
6 10 19.86 17.83 12.5 8.47 22.41 17.31 11.11 16.3910 14 15.82 16.96 14.41 14.12 18.82 21.25 12.5 15.05
1 3 16.67 14.81 32.65 20 21.62 20 5 11.111 3 16.67 14.81 32.65 20 21.62 20 5 11.11
522 655 17.31 17.07 16.72 16.83 16.05 16.85 15.6 16.9
182
APPENDIX B: GOVERNMENT (DEEWR) DATA ON STUDENTS COMMENCING ICT DEGREES
RFI 10-324 Roberts
2001 2002 200323896 23560 21220
Charles Sturt University 878 831 942Macquarie University 524 443 459Southern Cross University 238 227 257The University of New England 206 135 106
The University of New South Wales
858 628 418
The University of Newcastle 396 355 278The University of Sydney 299 478 446University of Technology, Sydney 727 759 796
University of Western Sydney 841 829 776University of Wollongong 993 1225 1021Sub-total 5960 5910 5499Deakin University 498 629 733La Trobe University 357 450 466Monash University 2500 2443 2040RMIT University 1564 1329 1405Swinburne University of Technology
1066 949 924
The University of Melbourne 525 447 397University of Ballarat 422 447 692Victoria University 823 983 804Sub-total 7755 7677 7461Central Queensland University 2407 2275 1354
Griffith University 612 653 527James Cook University 179 125 132Queensland University of Technology
1489 1492 1201
The University of Queensland 385 366 395University of Southern Queensland
721 756 671
University of the Sunshine Coast 59 34 45
Sub-total 5852 5701 4325Curtin University of Technology 569 536 457
Edith Cowan University 726 831 736
New South Wales
Victoria
Queensland
Western Australia
TOTAL
State/Institution - numbers
2004 2005 2006 2007 2008 2001 2002 2003 2004 2005 200620788 16871 14674 14073 14623 8548 7815 6183 5099 4144 3466
782 564 520 498 550 313 226 181 158 122 71344 377 282 244 245 232 165 146 123 126 83162 118 145 142 157 68 100 67 37 18 25324 165 128 106 79 57 39 21 33 35 25
338 361 352 333 310 305 181 115 79 60 60
332 347 440 394 405 94 84 67 81 77 110288 249 271 324 321 146 179 142 79 64 72781 734 523 593 678 306 297 229 209 187 177
474 493 317 242 254 228 230 204 80 182 59762 878 784 1000 1010 408 411 312 209 235 184
4587 4286 3762 3876 4009 2157 1912 1484 1088 1106 866656 568 472 466 464 184 219 192 121 137 90446 318 310 270 242 94 98 80 63 49 50
1905 1263 1049 890 853 1318 1179 882 658 391 3451372 1229 1258 1121 1196 677 503 442 420 311 352
677 562 738 848 901 424 321 229 148 133 149
321 286 269 224 143 293 225 175 112 65 631584 827 667 862 907 88 122 83 153 112 82
736 517 368 307 352 225 210 229 179 134 1127697 5570 5131 4988 5058 3303 2877 2312 1854 1332 12431922 1667 1036 620 754 529 642 353 321 298 223
500 409 316 299 362 164 170 96 114 81 40156 243 306 248 330 53 54 43 47 34 97974 775 644 795 695 537 472 369 254 221 166
367 283 269 169 187 149 116 108 104 80 68801 355 375 381 303 227 255 196 156 101 88
40 41 40 40 33 14 13 16 14 6 2
4760 3773 2986 2552 2664 1673 1722 1181 1010 821 684456 429 289 285 273 252 191 120 148 187 83
658 706 580 614 586 204 237 217 145 122 107
All studentsMales Females
2007 2008 2001 2002 2003 2004 2005 2006 2007 20083347 3876 32444 31375 27403 25887 21015 18140 17420 18499
85 107 1191 1057 1123 940 686 591 583 65770 70 756 608 605 467 503 365 314 31534 58 306 327 324 199 136 170 176 21516 21 263 174 127 357 200 153 122 100
57 64 1163 809 533 417 421 412 390 374
82 122 490 439 345 413 424 550 476 527100 80 445 657 588 367 313 343 424 401123 168 1033 1056 1025 990 921 700 716 846
35 45 1069 1059 980 554 675 376 277 299202 217 1401 1636 1333 971 1113 968 1202 1227804 952 8117 7822 6983 5675 5392 4628 4680 4961
71 94 682 848 925 777 705 562 537 55838 41 451 548 546 509 367 360 308 283
261 251 3818 3622 2922 2563 1654 1394 1151 1104290 330 2241 1832 1847 1792 1540 1610 1411 1526160 174 1490 1270 1153 825 695 887 1008 1075
66 48 818 672 572 433 351 332 290 191171 241 510 569 775 1737 939 749 1033 1148
76 91 1048 1193 1033 915 651 480 383 4431133 1270 11058 10554 9773 9551 6902 6374 6121 6328
208 248 2936 2917 1707 2243 1965 1259 828 1002
51 62 776 823 623 614 490 356 350 42490 107 232 179 175 203 277 403 338 437
201 213 2026 1964 1570 1228 996 810 996 908
50 51 534 482 503 471 363 337 219 238107 58 948 1011 867 957 456 463 488 361
4 5 73 47 61 54 47 42 44 38
711 744 7525 7423 5506 5770 4594 3670 3263 3408104 85 821 727 577 604 616 372 389 358
93 115 930 1068 953 803 828 687 707 701
Total
Murdoch University 307 343 223The University of Western Australia
208 264 212
Sub-total 1810 1974 1628The Flinders University of South Australia
139 178 146
The University of Adelaide 90 163 163University of South Australia 951 654 616Sub-total 1180 995 925University of Tasmania 394 394 576Sub-total 394 394 576Charles Darwin University 129 98 79Sub-total 129 98 79The Australian National University
159 157 153
University of Canberra 404 355 346Sub-total 563 512 499Australian Catholic University 253 299 228Sub-total 253 299 228
23896 23560 21220
Notes:ADFA students are included in Uni NSW, and AMC students are included in Uni Ta Includes only Infomation technology students
Northern Territory
Australian Capital Territory
Multi-State
Total
South Australia
Tasmania
194 185 192 210 232 83 76 66 30 38 35167 122 97 105 124 61 57 34 24 25 28
1475 1442 1158 1214 1215 600 561 437 347 372 253138 88 72 58 71 58 67 42 46 19 21
174 177 175 170 171 22 39 32 39 31 28635 612 523 370 276 332 223 236 184 129 78947 877 770 598 518 412 329 310 269 179 127725 452 462 389 636 101 115 274 361 203 177725 452 462 389 636 101 115 274 361 203 177
36 31 44 53 38 46 47 18 15 9 1136 31 44 53 38 46 47 18 15 9 11
145 125 103 135 162 50 42 30 26 27 28
222 180 163 177 226 146 129 88 59 58 52367 305 266 312 388 196 171 118 85 85 80194 135 95 91 97 60 81 49 70 37 25194 135 95 91 97 60 81 49 70 37 25
20788 16871 14674 14073 14623 8548 7815 6183 5099 4144 3466
asmania for all years
32 46 390 419 289 224 223 227 242 27845 21 269 321 246 191 147 125 150 145
274 267 2410 2535 2065 1822 1814 1411 1488 148218 16 197 245 188 184 107 93 76 87
31 27 112 202 195 213 208 203 201 19888 74 1283 877 852 819 741 601 458 350
137 117 1592 1324 1235 1216 1056 897 735 635168 395 495 509 850 1086 655 639 557 1031168 395 495 509 850 1086 655 639 557 1031
20 11 175 145 97 51 40 55 73 4920 11 175 145 97 51 40 55 73 4926 32 209 199 183 171 152 131 161 194
54 61 550 484 434 281 238 215 231 28780 93 759 683 617 452 390 346 392 48120 27 313 380 277 264 172 120 111 12420 27 313 380 277 264 172 120 111 124
3347 3876 32444 31375 27403 25887 21015 18140 17420 18499
183
APPENDIX C: ORIGINAL SURVEY TOOL FROM WEST ET AL. (1986)
184
APPENDIX D: TABLES SHOWING THE MAPPING OF SURVEY QUESTIONS
185
Main Theme Survey Item Accommodation I felt a lack of freedom and independence in living at home There was tension/conflict with or among people with whom I shared
accommodation There was a lack of co-operation in housekeeping duties in shared
student house There was too much loneliness in college/university residential
accommodation
Course I didn’t find the course interesting I didn’t get on well with other students in my course I didn’t get on well with the teachers in my course The teachers gave me too little encouragement or assistance I was unable to obtain extra help with my course
Institution The college/university environment felt unfriendly and impersonal There was little encouragement or enthusiasm for learning at my
college/university In general, teaching room conditions were not pleasant for learning Campus activities were too distracting There was nothing very interesting to do on or around campus The institution was not geared to meet my special needs I felt different and unwelcome because of my background Administrative staff seemed uncaring or uninterested Teaching staff seemed uncaring or uninterested
Distance/Remoteness I was depressed by the sheer inconvenience of daily travel to university/college
The problems with travel arrangements caused me to miss classes, get behind, etc.
Lengthy daily travel reduced the time I had on campus Lengthy daily travel isolated me from activities and friends outside
college and university Living away from home, I felt isolated from familiar people and
places I couldn’t afford to travel or phone home as often as I would have
liked I couldn’t get over feelings of homesickness, being away from home It was very hard getting used to a different lifestyle from the one I had
at home
Finance The differences between my background and that of other students was emphasised by lack of money
I was always worried about having enough money to get by from week to week
I hated not having enough money to keep up with other students/friends
Family Tertiary studies lead me to challenge family attitudes
Health I felt depressed and lonely at university/college Conflict or tension in my home environment affected my physical
186
and/or mental health I had to help care for a sick relative/friend which left me little time to
study I felt stressed and worried about my progress in my course I felt overworked and exhausted in coping with my course I became ill trying to combine work and study A health condition developed or worsened during the year and I had
to withdraw from studies My physical health suffered because of a poor diet
Personal Decision I didn’t like being a student A close personal friend decided to withdraw and I followed suit
Academic Preparedness I couldn’t keep up with the other students in my course I lacked important pre-requisite knowledge and found I couldn’t cope
with the course content My maths background was insufficient to cope with the course I was “burnt out” by the effort of qualifying for entry Table 1D: Factors from the West et al. (1986) survey that fit Tinto’s (1975) Social Integration Model
Main Theme Survey Item Accommodation There was too much pressure from parents to study I felt a lack of freedom and independence in living at home My family didn’t appreciate the demands of my course I had inadequate study facilities at home There was tension/conflict within my family There was a lack of quietness for study in shared housing There was tension/conflict with or among people with whom I shared
accommodation There was a lack of co-operation in housekeeping duties in shared
student house There was too much loneliness in college/university residential
accommodation There were too many distractions from study in student residential
accommodation The time needed to shop, cook, carry out household duties, etc.
Interfered with study I needed to change accommodation during term
Course I didn’t find the course interesting I only wanted/needed to do part of the course The course lacked relevance to my plans I found the course too difficult The course was not what it appeared to be in the pre-course
information I was dissatisfied with the teaching in the course The course was not my first choice
187
I was unable to obtain extra help with my course The workload was too heavy I discovered there were no job opportunities in this field I thought I was going to fail
Institution There was little encouragement or enthusiasm for learning at my college/university
In general, teaching room conditions were not pleasant for learning Campus activities were too distracting The institution was not geared to meet my special needs The institution was too inflexible in its administration arrangements Student facilities on campus were poor Academic resources were inadequate
Distance/Remoteness I was depressed by the sheer inconvenience of daily travel to university/college
There was interference of travel with study because of the time, tiredness, stress, involved in daily travel
The problems with travel arrangements caused me to miss classes, get behind, etc.
Lengthy daily travel isolated me from activities and friends outside college and university
I felt unsafe on public transport after late classes Travel problems caused financial burdens I couldn’t afford to travel or phone home as often as I would have
liked I could not find suitable accommodation near the institution Travel problems caused financial burdens
Finance I couldn’t earn enough through part-time work to pay for living expenses, rent etc.
I couldn’t get a part-time job so couldn’t afford to continue studying I was always worried about having enough money to get by from
week to week I had a major unexpected expense which I couldn’t meet I didn’t get TEAS (Tertiary Education Assistance Scheme) so I wasn’t
able to commence/continue with my studies My TEAS allowance was inadequate By the time my first TEAS payment arrived my financial problems
had become too great Payments of TEAS were too erratic to rely on I lost TEAS I didn’t like being dependent on my parents for money, so I decided to
work instead of study I wanted a more comfortable lifestyle so decided to leave study and
get a job I underestimated the expenses involved in living away from home I had to leave study and get a full-time job to support my family
Family I had difficulties with jugging family commitments and study
188
demands There were problems in arranging babysitting/child care to enable
attendance at college/university There was an interruption to or distraction from my study because of
needs/demands of family members I became pregnant during my course Tertiary studies lead me to challenge family attitudes
Health I became ill trying to combine work and study A health condition developed or worsened during the year and I had
to withdraw from studies My physical health suffered because of a poor diet
Personal Decision I needed a break from study to think about my life and where I was going
I was tired of studying I didn’t like being a student I changed my career plans so my course was no longer relevant I changed my immediate life plans and study was not longer
necessary I had doubts about the value of the course I was doing Withdrawing was the only way of resolving the conflicts that being a
student created in my life I never really wanted to continue studying
Job I left my course to look for a job I found a job which interested me and which didn’t require
completion of my course I was only doing the course until I found a job Part-time work didn’t give me enough income to continue studying,
so I got a full-time job I found a job which enabled me to continue studying only on a part-
time basis I was tired of juggling work and study so I decided to quit study I needed a permanent full-time job to continue studying by couldn’t
find one I needed vacation work to continue with my studies, but couldn’t find
any I lost the job I relied upon for my continued study
Academic Preparedness My study skills were not satisfactory I had difficulties in organising my study time I lacked important pre-requisite knowledge and found I couldn’t cope
with the course content My maths background was insufficient to cope with the course I was “burnt out” by the effort of qualifying for entry Table 2D: Factors from the West et al. (1986) survey that fit Bean’s (1980) Rational Decision Model
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APPENDIX E: SURVEYMONKEY STUDENT SURVEY PAGES INCLUDING WELCOME PAGE
Page 1
Student Progress SurveyStudent Progress SurveyStudent Progress SurveyStudent Progress Survey
Thank you for deciding to complete this survey. As a student who has withdrawn or transferred from your ICT degree your contribution will be most valuable in helping our understanding of the contributing factors that lead to this decision and our ability to make future improvements. How to complete the survey 1. The survey consists of a set of statements for you to rate. It also gives space for you to comment on your experience or other issues we may have overlooked. 2. You will be asked to rate each item for importance, so simply click on the square which best represents your point of view. 3. It would be best to allocate at least 15 minutes to complete the survey. What will happen to the results 1. They will be used to improve the quality of both course design and the student experience for students who take courses like the one you did. 2. We will send you a summary of our findings if you indicate that you would like this by contacting the project manager, Dr Tony Koppi, at the University of Wollongong ([email protected]). Your participation in this research is voluntary and you are free to withdraw at any time. All your responses will be treated in strictest confidence, and your answers and comments will not be published in any form that identifies you. This study has been approved by the University of Wollongong Ethics Committee (Approval Number HE10/198). If you have any complaints or reservations about any aspect of your participation in this research you may contact the project manager, or the Human Research Ethics Committee, Research Office, University of Wollongong NSW 2522 or phone (02) 4221 3386. Any complaint you make will be treated in confidence and investigated fully, and you will be informed of the outcome.
1. Understanding Why ICT Students Withdraw from their Degrees
If
If
Page 2
Student Progress SurveyStudent Progress SurveyStudent Progress SurveyStudent Progress Survey
6. Did your drop your ICT course because of:
2. Finding Out a Little Bit About You
1. Please select your age when you enrolled in your ICT degree:
Age
Age when you enrolled 6
2. Please select your gender: Gender
Gender 6
3. Please select the ICT discipline in which you were first enrolled at University:
ICT Discipline
ICT Discipline 6
4. For the discipline you chose at question 3, please select when you decided to drop that course at University:
Withdraw/transfer from Course
Withdrew/transferred 6
5. Please select whether you were a full time or part time student when you enrolled in the ICT degree:
Enrolment Status
Enrolment Status 6
Other ICT Discipline (please specify)
Personal reasons
nmlkj
Something about the course
nmlkj
Both of the above
nmlkj
Page 3
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7. When thinking about your experience, how would you rate the following, using the scale below?
3. How Your Experiences at University Affected Your Decision
Strongly disagree
DisagreeNeither agree nor disagree
AgreeStrongly agree
Attending evening classes posed a security risk. gfedc gfedc gfedc gfedc gfedc
I couldn’t get help when I needed it. gfedc gfedc gfedc gfedc gfedc
There were no opportunities to socialise. gfedc gfedc gfedc gfedc gfedc
The University facilities were not adequate. gfedc gfedc gfedc gfedc gfedc
There were too many distractions preventing me from concentrating on my studies.
gfedc gfedc gfedc gfedc gfedc
The University staff were not friendly. gfedc gfedc gfedc gfedc gfedc
The academic environment did not suit my learning style. gfedc gfedc gfedc gfedc gfedc
Organising a suitable timetable, with no clashes, was challenging. gfedc gfedc gfedc gfedc gfedc
Page 4
Student Progress SurveyStudent Progress SurveyStudent Progress SurveyStudent Progress Survey
8. When thinking about your university ICT course how would you rate the following, using the scale below?
4. How Your Experience of the ICT Course Affected Your Decision
Strongly disagree
DisagreeNeither agree nor disagree
AgreeStrongly agree
The teaching methods were harsh and confrontational. gfedc gfedc gfedc gfedc gfedc
The teachers didn’t explain the exercises. gfedc gfedc gfedc gfedc gfedc
The pace of teaching was too fast. gfedc gfedc gfedc gfedc gfedc
The classes were boring. gfedc gfedc gfedc gfedc gfedc
The teachers were not prepared. gfedc gfedc gfedc gfedc gfedc
The teaching environment was not welcoming. gfedc gfedc gfedc gfedc gfedc
The teachers’ knowledge was out of date. gfedc gfedc gfedc gfedc gfedc
The course content was male oriented. gfedc gfedc gfedc gfedc gfedc
Page 5
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9. When thinking about your university ICT course how would you rate the following, using the scale below?
5. How Your Experience of the ICT Course Affected Your Decision 2
Strongly disagree
DisagreeNeither agree nor disagree
AgreeStrongly agree
The course was too competitive. gfedc gfedc gfedc gfedc gfedc
The course didn’t have a business focus. gfedc gfedc gfedc gfedc gfedc
The course was too theoretical. gfedc gfedc gfedc gfedc gfedc
I didn’t understand the concepts. gfedc gfedc gfedc gfedc gfedc
The course lacked practical applications. gfedc gfedc gfedc gfedc gfedc
I wasn’t encouraged to do well by the teachers. gfedc gfedc gfedc gfedc gfedc
The course didn’t have a workplace focus. gfedc gfedc gfedc gfedc gfedc
The course was too mathematical. gfedc gfedc gfedc gfedc gfedc
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10. When thinking about your university ICT course how would you rate the following, using the scale below?
6. How Your Experience of the ICT Course Affected Your Decision 3
Strongly disagree
DisagreeNeither agree nor disagree
AgreeStrongly agree
The focus was on individual activities rather than groups. gfedc gfedc gfedc gfedc gfedc
I didn’t have the expected background knowledge. gfedc gfedc gfedc gfedc gfedc
Students acted or spoke in a sexist manner. gfedc gfedc gfedc gfedc gfedc
There were too many assignments. gfedc gfedc gfedc gfedc gfedc
My results were not as high as I expected. gfedc gfedc gfedc gfedc gfedc
The course was poorly structured. gfedc gfedc gfedc gfedc gfedc
I felt it was unacceptable to be smart. gfedc gfedc gfedc gfedc gfedc
I didn’t understand the meaning of the terms used in the course. gfedc gfedc gfedc gfedc gfedc
The course didn’t meet my expectations. gfedc gfedc gfedc gfedc gfedc
Page 7
Student Progress SurveyStudent Progress SurveyStudent Progress SurveyStudent Progress Survey
11. When thinking about your university ICT course how would you rate the following, using the scale below?
7. How Your Experience of the ICT Course Affected Your Decision 4
Strongly disagree
DisagreeNeither agree nor disagree
AgreeStrongly agree
I didn’t feel I fitted in or belonged. gfedc gfedc gfedc gfedc gfedc
I didn’t enjoy attending the classes. gfedc gfedc gfedc gfedc gfedc
Male staff didn’t encourage me to participate. gfedc gfedc gfedc gfedc gfedc
There were no or few females in the classes. gfedc gfedc gfedc gfedc gfedc
Male students wouldn’t let me participate. gfedc gfedc gfedc gfedc gfedc
Male staff acted or spoke in a sexist manner. gfedc gfedc gfedc gfedc gfedc
I was in the minority in my classes. gfedc gfedc gfedc gfedc gfedc
I didn’t make friends with classmates. gfedc gfedc gfedc gfedc gfedc
Page 8
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12. When thinking about your life outside the university, how would you rate the following, using the scale below?
8. How Your Life Outside University Affected Your Decision
Strongly disagree
DisagreeNeither agree nor disagree
AgreeStrongly agree
Living away from home was too difficult. gfedc gfedc gfedc gfedc gfedc
My timetable didn’t fit with my work commitments. gfedc gfedc gfedc gfedc gfedc
I or my partner got pregnant. gfedc gfedc gfedc gfedc gfedc
Living at home was too difficult. gfedc gfedc gfedc gfedc gfedc
Travelling to University was/is difficult because of distance. gfedc gfedc gfedc gfedc gfedc
Living in student accommodation was too difficult. gfedc gfedc gfedc gfedc gfedc
My timetable didn’t fit with the transport timetable. gfedc gfedc gfedc gfedc gfedc
Travelling to University was/is difficult because of transport. gfedc gfedc gfedc gfedc gfedc
I became very ill or was involved in a serious accident. gfedc gfedc gfedc gfedc gfedc
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13. When thinking about your life outside the university, how would you rate the following, using the scale below?
14. From the questions about why you left/changed your degree, which had the most influence on your decision and why?
15. Are there any comments you would like to add about your experience, especially if these questions did not cover something that was important to you?
9. How Your Life Outside University Affected Your Decision 2
Strongly disagree
DisagreeNeither agree nor disagree
AgreeStrongly agree
A family member died or was very ill or had a serious accident. gfedc gfedc gfedc gfedc gfedc
I lost my job. gfedc gfedc gfedc gfedc gfedc
My family didn’t help me to study at home. gfedc gfedc gfedc gfedc gfedc
I couldn’t get financial aid. gfedc gfedc gfedc gfedc gfedc
I missed my family. gfedc gfedc gfedc gfedc gfedc
Studying at University wasn’t as important as socialising with my friends.
gfedc gfedc gfedc gfedc gfedc
I picked the wrong degree. gfedc gfedc gfedc gfedc gfedc
Attending University was too expensive. gfedc gfedc gfedc gfedc gfedc
There was conflict with my work commitments. gfedc gfedc gfedc gfedc gfedc
55
66
55
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17. Please indicate the ranking of you final grade in High School:
18. When thinking about your ICT degree, was it your first choice?
19. If the ICT degree was not your first choice, please state why you enrolled in the ICT degree as your second choice:
20. When thinking about the year you enrolled in your ICT degree, was it the first year of enrolment in any degree?
10. A Little Bit More About You, and Then You're Done!
16. Please select the range that best describes the number of hours you worked each week while being a student:
Hours Worked Per Week
Hours worked per week 6
55
66
21. Did you miss the beginning of your course?
Top 10%
nmlkj
Next 20%
nmlkj
Middle 40%
nmlkj
Lower 20%
nmlkj
Bottom 10%
nmlkj
Yes, it was my first choice of degree
nmlkj
No, there was another degree I wanted to do
nmlkj
The name (or approximate name) of the degree that was your first choice:
nmlkj
Yes, it was the first year
nmlkj
No, I enrolled in a different degree first
nmlkj
The name of the first degree in which you enrolled:
nmlkj
Yes
nmlkj No
nmlkj
If Yes, please state why you missed the beginning of your course.
nmlkj
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24. Did you visit your university during Orientation Week?
25. During your time as an ICT student, did you attend functions organised by your school/faculty/department?
26. How would you rate your performance in the ICT degree before you withdrew or transferred?
28. What language do you speak at home?
22. Were you enrolled as an undergraduate or postgraduate?
23. Were you enrolled as a domestic or international student?
27. When you were enrolled as an ICT student, what was your marital status?
Marital Status
Marital Status 6
Undergraduate
nmlkj Postgraduate
nmlkj
Domestic
nmlkj International
nmlkj
Yes
nmlkj
No
nmlkj
If No, please say why you didn't attend Orientation Week:
nmlkj
55
66
Yes
nmlkj
No
nmlkj
If No, please say why you didn't attend the functions:
nmlkj
55
66
Top 10%
nmlkj
Next 20%
nmlkj
Middle 40%
nmlkj
Lower 20%
nmlkj
Bottom 10%
nmlkj
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31. If you would like to enter the draw for the Coles/Myer voucher, please supply your name, email address and phone number. These details will be separated from your survey answers so that you will remain anonymous.
32. If you are willing to participate in an interview, at a time and place convenient to you, please supply your name, email address and phone number. These details will be separated from your survey answers so that you will remain anonymous.
29. Please select the range that best describes your parents'/guardians' approximate combined annual income before taxes:
Parents'/Guardians' Income before Taxes
Parents'/Guardians' Income 6
30. Please select the country in which you were born/your ethnicity:
Origin/Ethnicity
Country of origin or ethnicity 6
55
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55
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Other (please specify)
190
APPENDIX F: TEXT FOR INCLUSION IN CORRESPONDENCE TO EX-ICT STUDENTS
«AddressBlock» «GreetingLine»
<ON FACULTY LETTERHEAD> RE: ALTC PROJECT SURVEY OF ICT STUDENTS Your University, as part of a consortium between the universities of Murdoch, Queensland, Swinburne and Wollongong, is contributing to a joint ALTC project examining four areas of concern for the Information and Communications Technology (ICT) discipline. Our project aims to:
1. Understand the poor and erroneous perceptions that students seem to have of ICT 2. Understand the motivations of ICT students, lack of women and attrition 3. Understand the role of industry in the design and implementation of the ICT curriculum 4. Understand the teaching-research-industry-learning (TRIL) nexus.
Having received acceptance of our proposal by the Ethics Committee of the University of Wollongong (HE10/198), we are now in a position to request your assistance to gather data on students in two categories. Those who have: (1) withdrawn from an ICT degree and are no longer studying at your University; and (2) withdrawn from their ICT degree but are continuing their study in another discipline at your University. We acknowledge that ICT is an area of study that encompasses a variety of degrees and offer the following as examples: BEng(Electrical), BCompSci, BMath, BScIS, BComIS, BIT and BMath(IT) and their postgraduate equivalents. The students we are most keen to contact are those who have withdrawn or transferred from an ICT degree from 2005 to 2009 or during the Autumn Session of 2010. It is our intention to invite them – by email or letter – to complete an online survey and, subsequently, to volunteer to participate in an interview, either face-to-face or by phone, so that we can gain a greater understanding of their motivations and experiences which may explain the high attrition rate of ICT students from Australian universities. To achieve this, we are seeking your assistance in contacting these students via their student email accounts or permanent addresses. We would like to request that you accept our text (supplied on the following page) and undertake to send it out to the students you identify as qualifying to participate. In addition, we would also ask that you supply us with data on: (1) the number of students you identify in each category (withdrawn or transferred); (2) the first year of their enrolment in an ICT degree; (3) whether their enrolment was as an under or post graduate; (4) their gender; (5) their status as domestic or international student; (6) their enrolment status (full-time or part-time); (7) their age: and (8) the number who failed to meet academic standards for continuation. We look forward to receiving your response to our request. Sincerely Professor Philip Ogunbona ALTC Project Leader and Dean of Informatics University of Wollongong
We have contacted you because we need your help. Our records show that you have either withdrawn from an ICT degree or transferred from an ICT degree to another degree. This attrition from ICT degrees is a serious problem for the ICT industry and we are conducting a national study to try to understand why students leave ICT degrees and to try to improve the student experience of ICT courses. To do that, we need your help. Our goal is to improve courses and experiences for future students by understanding what experiences result in students choosing not to pursue a tertiary education in ICT. The study involves a short online survey which will take about 15 minutes to complete. All responses given will remain confidential and anonymous and you can withdraw at any time. Participation in the survey is entirely voluntary but once you submit you responses you’ll be giving your consent for us to use the data you have provided. To thank you for helping us, on completing the survey, you will have the opportunity to enter the draw for a Coles/Myer voucher worth $100 by providing your name and contact details which will not be linked to your survey response and only be used in the prize draw. We will also ask you to indicate your willingness to participate in an interview – either face-to-face or by telephone – and this will also be entirely voluntary and the data you supply will not identify you in any way.
Please read through the first page of the survey as it gives you details about confidentiality and anonymity.
The survey can be found at:
www.surveymonkey.com/s/ictstudentsurvey
If you are interested, we would be more than happy to provide you with feedback on the project. If you require more information, please contact Madeleine Roberts (0418 692 263) or by email: [email protected]. Thank you for choosing to participate in this survey.
191
APPENDIX G: SURVEYMONKEY SPREADSHEETS
ResearchIDRespondentID Age Enrolled CaAge Enrolled Gender ICT Discipl 1=F 2=M 1=CS, 2=C
1 1294264213 4 20 2 82 1291355892 2 18 2 43 1286867510 5 21 2 44 1278751340 2 18 2 15 1272678924 10 26-35 2 86 1267119605 4 20 1 47 1264152499 8 24 1 18 1261317031 2 18 2 19 1259787649 2 18 2 1
10 1250825926 2 18 1 111 1243301409 1 Under 18 2 712 1242116714 2 18 2 513 1241177930 11 36-45 2 314 1240894291 12 46-55 2 815 1240065803 7 23 2 116 1239487189 2 18 2 117 1238218082 4 20 2 118 1238045746 2 18 2 819 1238030558 2 18 2 820 1237218987 5 21 2 421 1236632010 6 22 2 322 1236378743 1 Under 18 2 223 1235471587 9 25 2 124 1235398153 2 18 2 425 1235391386 1 Under 18 2 126 1235373784 2 18 2 327 1235299434 2 18 2 828 1235237718 4 20 2 629 1235200731 10 26-35 2 330 1234185863 5 21 2 831 1234181529 2 18 2 432 1234128218 12 46-55 1 533 1234122771 10 26-35 2 134 1234042786 2 18 2 135 1234005306 2 18 2 536 1233861539 1 Under 18 2 437 1227047382 1 Under 18 1 438 1226938565 2 18 1 439 1226692993 6 22 2 440 1226428767 1 Under 18 2 441 1226397474 1 Under 18 1 442 1225990386 8 24 1 843 1223703700 7 23 2 444 1223682524 1 Under 18 2 445 1223600896 1 Under 18 2 446 1223487981 2 18 2 447 1223407195 2 18 2 448 1222587584 1 Under 18 2 449 1222443274 2 18 1 450 1222239120 2 18 2 451 1222227485 4 20 2 4
Other ICT Discipline Withdrew/transferred D Withdrew/transferred PFT_PT Personal or co Academic CSE, 3=EE, 4=IT, 5=IS, 6=SE, 7=TE, 8=other 1=FT 2=PT1=course 2=p 1= SD 5=S
Games tech 5 Between 1 July and 31 1 3 58 Between 1 July and 31 1 3 3
Media 8 Between 1 July and 31 1 3 47 Between 1 February an 1 3 4
Master of Computer Stud 11 Between 1 July and 31 1 2 41 Between 1 February an 1 3 2
21 Between 1 and 30 Sept 2 3 28 Between 1 July and 31 1 1 28 Between 1 July and 31 1 3 47 Between 1 February an 1 2 42 Between 1 July and 31 1 3 41 Between 1 February an 1 3 2
23 Between 1 and 30 Nov 1 3 3Statistics 15 Between 1 and 31 Mar 2 1 1
11 Between 1 July and 31 1 3 210 Between 1 February an 1 2 27 Between 1 February an 1 2 2
Internet Science & Tech 14 Between 1 and 28 Feb 1 3 3Internet Science & Tech 14 Between 1 and 28 Feb 1 3 3
11 Between 1 July and 31 1 3 57 Between 1 February an 2 3 2
21 Between 1 and 30 Sept 2 3 58 Between 1 July and 31 1 3 1
19 Between 1 and 31 July 1 3 410 Between 1 February an 1 3 47 Between 1 February an 1 1 2
Bachelor of Internet Scie 7 Between 1 February an 1 3 21 Between 1 February an 2 1 4
14 Between 1 and 28 Feb 1 3 2Computer Engineering 11 Between 1 July and 31 2 2 5
1 Between 1 February an 1 3 315 Between 1 and 31 Mar 2 3 4
I was a PHD student. 7 Between 1 February an 1 3 57 Between 1 February an 1 1 4
18 Between 1 and 30 June 1 3 21 Between 1 February an 1 2 12 Between 1 July and 31 1 3 2
16 Between 1 and 30 Apri 1 3 519 Between 1 and 31 July 2 1 218 Between 1 and 30 June 1 3 41 Between 1 February an 1 2 2
Information Environmen 1 Between 1 February an 2 3 15 Between 1 July and 31 1 3 4
30 Between 1 and 28 Feb 1 31 Between 1 February an 1 3 3
19 Between 1 and 31 July 1 2 420 Between 1 and 31 Aug 1 3 45 Between 1 July and 31 1 3 2
17 Between 1 and 31May 1 3 57 Between 1 February an 1 2 31 Between 1 February an 1 2 4
Attending e Organising I couldn’t g The Univer There were The Univer There were The pace o The classes 1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S
3 5 3 2 2 3 3 4 52 4 3 3 4 3 3 3 43 4 4 5 3 5 4 2 42 5 1 3 1 1 1 1 52 2 4 5 3 4 4 41 1 2 1 1 1 1 1 12 5 2 2 2 4 4 4 42 4 4 3 1 2 5 2 41 1 1 1 1 1 4 2 42 2 3 2 3 2 2 3 42 2 2 1 1 3 3 4 32 2 2 2 2 2 2 2 41 1 5 3 4 1 2 1 11 1 1 1 1 1 3 2 21 1 1 1 1 1 4 1 11 1 2 1 1 4 1 4 41 2 5 5 1 2 1 2 51 2 3 4 2 2 2 5 51 2 2 4 2 3 3 5 44 4 4 3 2 4 5 3 32 3 2 2 2 2 2 3 22 5 4 2 2 3 4 5 33 3 1 1 1 2 4 2 23 5 4 4 3 2 4 5 45 4 5 4 3 3 4 4 51 3 2 2 1 2 2 3 22 5 4 3 2 2 4 4 42 3 3 2 2 3 4 2 42 2 2 1 3 2 4 3 21 5 4 4 3 5 4 1 11 1 2 3 3 2 4 3 53 3 5 4 3 4 3 5 33 3 5 5 4 4 4 3 31 3 2 2 2 1 2 2 22 2 4 3 2 2 2 2 23 4 3 3 2 1 2 4 42 2 2 2 2 2 2 2 42 3 5 3 4 4 4 5 42 2 2 2 2 2 4 3 32 2 3 3 3 2 4 4 31 4 2 2 2 1 4 2 43 1 3 1 3 1 1 4 13 3 4 2 3 3 3 5 5
1 1 2 1 2 2 5 2 33 4 5 5 5 5 3 4 52 2 2 2 2 1 2 2 32 2 3 4 2 2 4 3 31 2 4 2 2 1 2 5 51 1 1 1 2 4 1 1 21 2 4 2 3 2 1 5 5
The teachin The teache The teache The course The teache The teachin I wasn’t en The course The course 1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S
3 4 3 3 4 3 4 2 53 4 3 4 3 4 3 3 44 4 3 3 3 4 4 4 41 5 4 1 1 1 3 1 13 5 2 2 3 5 4 2 31 1 1 1 1 1 2 2 22 3 2 3 2 2 3 3 22 2 2 2 2 3 2 2 21 1 1 1 1 1 1 1 13 3 2 3 2 3 4 2 32 4 2 1 2 2 1 2 21 1 2 2 1 1 1 1 11 1 1 1 1 1 4 2 22 2 2 2 2 2 2 2 21 1 1 1 1 1 1 1 12 3 3 3 1 2 2 2 22 5 2 2 5 5 3 1 43 3 2 1 2 4 3 3 53 4 2 1 3 4 4 3 53 3 5 3 4 3 5 32 2 2 2 2 2 2 2 22 3 2 3 2 4 4 2 21 1 1 2 1 1 1 1 14 4 3 2 3 4 4 4 43 5 5 3 5 5 5 3 41 2 2 3 1 2 1 1 22 4 2 2 2 3 4 2 42 2 2 2 2 3 3 2 32 2 2 2 2 2 2 2 22 2 3 3 3 5 4 3 23 2 4 5 2 3 2 3 33 5 3 3 3 4 3 3 33 3 5 3 5 5 3 3 32 2 2 2 2 2 2 1 22 2 3 2 2 2 3 2 53 2 3 3 2 3 3 4 42 2 2 2 2 2 3 3 43 5 3 5 3 4 4 5 42 3 2 2 2 2 2 2 33 2 2 4 2 3 3 3 32 4 2 2 2 4 4 4 42 2 1 1 1 2 1 1 14 3 3 3 2 3 3 3 4
2 2 1 3 2 2 3 4 24 5 3 5 3 5 4 3 42 2 4 2 3 2 3 3 44 4 3 5 3 4 4 3 32 4 2 4 2 3 2 3 21 2 4 1 2 3 1 2 14 3 1 1 2 2 3 4 5
I didn’t und The course The course The course The course I didn’t hav The course The course I didn’t und 1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S
3 3 5 5 5 2 4 4 33 2 3 2 43 4 4 3 4 1 4 5 31 1 1 1 5 5 5 5 12 5 4 4 5 1 5 5 24 4 2 2 2 2 2 5 24 2 2 2 3 4 2 3 41 2 4 3 4 2 1 1 22 1 3 2 3 2 1 5 14 2 3 4 4 3 3 4 44 5 3 2 3 4 5 4 44 1 1 1 1 2 2 4 22 2 2 2 2 1 5 4 12 2 2 2 2 2 2 2 21 1 1 1 1 1 1 1 14 1 4 4 4 1 5 5 13 3 4 1 4 2 4 2 44 2 3 4 5 5 3 4 44 2 3 4 5 4 3 4 33 3 3 5 4 3 5 4 22 2 2 2 2 4 2 2 24 2 3 2 2 4 2 4 42 1 1 2 2 2 3 2 24 4 3 4 4 3 3 4 35 3 5 4 5 2 5 5 42 2 5 2 2 3 2 1 24 2 4 4 4 4 4 5 32 2 4 4 4 2 3 4 22 2 2 2 2 4 2 2 22 2 2 2 3 2 5 2 22 3 4 3 4 2 4 4 23 3 3 3 3 3 4 5 44 3 3 3 3 1 3 3 32 2 1 1 2 3 2 2 22 2 1 5 5 3 4 3 23 4 4 4 4 3 4 3 44 5 4 4 4 4 4 5 55 4 4 4 4 5 3 4 52 2 3 2 3 3 2 3 24 5 3 2 2 2 3 34 2 3 4 3 3 4 4 43 1 5 5 5 3 1 4 14 4 3 4 3 4 3 3 4
4 2 4 3 4 2 2 4 34 3 3 4 4 2 5 5 42 3 3 2 3 2 3 4 23 3 3 3 3 3 3 4 35 4 2 2 3 5 2 5 51 1 1 2 1 1 4 3 15 5 5 4 4 2 3 4 5
I felt it was There were My results Students a The focus w I didn’t fee I didn’t enj Male staff d I didn’t ma 1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S
3 4 3 3 3 3 5 3 3
3 4 4 3 3 3 4 3 31 1 1 1 3 4 5 5 13 5 4 3 3 3 3 2 22 2 2 2 2 1 4 1 12 3 4 2 3 4 4 2 31 4 2 1 2 1 4 1 11 4 4 1 1 4 4 1 12 3 4 3 4 4 4 4 41 2 4 1 5 3 3 1 22 2 1 1 2 1 4 1 21 1 1 1 1 3 2 2 32 2 2 2 2 1 1 1 11 1 1 1 1 1 1 1 11 1 2 5 2 1 2 1 12 4 2 2 4 4 5 2 42 3 4 1 2 4 5 1 52 3 4 1 3 4 5 1 52 3 4 3 1 5 5 3 52 2 4 2 3 2 4 2 22 3 4 2 5 4 4 2 41 3 3 1 1 2 1 1 22 5 4 2 2 2 4 3 23 5 4 2 5 2 5 2 21 3 2 2 4 1 2 1 22 5 5 2 3 4 5 2 22 3 2 1 3 3 4 1 21 2 3 2 1 2 2 2 22 2 5 1 3 4 4 3 32 3 3 2 3 2 4 2 43 3 3 3 3 4 4 3 33 3 1 3 3 5 3 3 32 1 2 1 1 1 2 3 32 2 2 2 2 4 4 2 41 3 3 2 3 3 3 3 22 2 5 2 2 4 4 4 41 4 4 2 5 5 5 4 42 2 2 2 3 2 3 2 32 2 3 2 3 3 4 3 42 4 4 4 2 3 4 3 21 3 5 1 1 3 1 1 43 3 4 3 3 3 4 3 2
2 3 2 2 2 2 2 31 3 3 4 1 4 5 5 42 2 3 2 2 4 4 2 22 4 3 2 2 3 4 2 31 2 4 2 2 5 5 1 21 1 1 1 1 1 1 1 12 3 4 1 4 5 4 2 2
Male staff a I was in th There were Male stude My timetab Living at ho My timetab Living away Travelling t 1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S
3 3 3 3 3 3 5 3 5
3 3 3 3 4 3 5 3 51 3 5 1 4 1 5 1 52 3 4 2 2 2 2 2 11 1 4 1 1 1 1 1 12 4 4 2 5 2 2 2 21 1 4 1 4 5 1 5 11 1 4 1 2 1 1 3 12 4 4 3 3 2 2 3 21 1 5 1 4 1 4 1 41 1 4 1 1 1 1 1 12 2 2 2 2 2 2 2 21 1 1 1 1 2 1 2 41 1 1 1 1 1 1 1 11 1 5 1 1 1 4 3 51 2 5 1 1 1 4 1 41 1 3 1 3 3 3 3 31 1 2 1 3 3 3 3 33 5 3 1 2 3 4 2 32 2 3 2 3 2 2 2 42 4 4 4 4 4 4 2 21 4 2 1 4 2 3 3 53 3 4 2 5 4 5 5 52 2 5 2 5 2 5 5 52 1 5 1 1 2 4 1 21 4 3 2 5 2 2 2 41 3 4 1 5 3 3 3 42 2 2 2 2 2 2 2 21 5 3 3 5 3 1 1 52 2 4 2 3 2 2 3 43 3 3 3 3 3 3 3 33 3 3 3 3 3 3 5 41 2 4 2 4 2 5 3 52 4 4 2 2 3 2 3 22 3 5 2 2 2 3 1 12 4 4 2 2 2 2 2 23 4 5 2 2 2 3 2 22 2 4 2 4 2 2 3 22 4 2 2 2 2 1 23 4 4 3 3 3 3 3 31 4 1 1 3 1 1 1 13 3 3 3 3 3 4 3 4
1 2 1 2 1 2 13 5 5 3 1 1 1 1 12 2 4 23 3 5 3 3 3 3 3 32 1 3 2 1 1 1 3 11 1 1 1 1 1 1 1 11 2 4 1 2 1 2 1 1
Living in st Travelling t I or my par I became v Studying at There was My family d I couldn’t g I lost my jo1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=S
3 5 3 3 3 3 4 1 3
3 5 3 4 3 4 3 3 31 5 1 1 1 4 1 1 11 2 2 2 2 2 2 2 21 1 1 1 1 1 1 1 12 2 3 2 2 5 2 2 24 2 1 1 1 4 5 5 13 1 1 1 2 3 1 1 12 2 2 2 2 2 2 2 21 4 1 2 1 4 1 1 11 1 1 1 1 1 1 1 12 2 2 2 2 2 2 2 23 3 1 1 1 1 1 1 11 1 1 1 1 4 1 3 13 4 1 1 1 1 1 1 13 4 1 1 1 1 1 1 13 3 3 3 3 3 3 3 33 3 3 1 3 3 3 3 32 4 3 3 3 3 3 32 2 2 2 2 5 2 2 23 2 1 3 2 4 5 3 43 5 1 5 2 3 3 4 33 5 1 2 2 4 4 5 35 5 1 1 4 4 4 5 11 3 1 1 3 1 1 1 14 2 2 2 2 4 4 5 23 3 3 3 3 5 3 3 33 2 2 2 2 2 2 2 23 2 4 1 1 5 3 3 32 2 1 1 4 2 2 2 23 4 3 3 3 3 3 3 33 4 4 2 4 2 2 5 43 5 1 2 2 4 3 2 33 2 2 2 2 2 3 2 23 2 3 3 3 3 2 3 32 2 2 2 2 2 2 2 22 2 1 2 2 2 2 2 22 3 2 2 2 4 3 4 2
2 1 1 2 1 3 1 13 3 3 3 3 3 3 3 31 1 5 1 1 1 1 5 13 4 3 3
2 1 1 4 1 1 2 3 11 1 1 1 3 1 1 1 1
1 1 1 5 13 4 3 3 4 2 2 4 23 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 11 2 1 1 1 1 1 1 1
A family m I missed m Attending U I picked th Main reason and why?1= SD 5=S1= SD 5=S1= SD 5=S1= SD 5=SOpen-ended
3 2 5 2 It was not fun.
3 3 3 4 Campus was in Ipswich, I had personal p 1 1 1 3 Probably the effort of the academic staff 2 2 2 2 I left the degree because I have been to 1 1 1 5 I left the Information Technology degree 2 2 2 3 The course was very inflexible in that the 1 1 5 1 I left home to go to university over 400k 1 1 5 5 The fact that I did not enjoy the course c 2 2 2 4 The subject material was boring, and did 1 1 1 4 Structure of the course and not enough b 1 1 1 5 I got accepted in the Bachelor of Comme2 2 2 2 This questionnaire does not address som 1 1 1 1 I could not find suitable accommodation1 1 1 1 Work commitments/lack of financial aid ( 1 1 1 2 The major that I took on (Multimedia an 1 1 1 5 the quality of the lecturers! I had proble 3 3 5 3 *Prerequiste courses were responsible fo 3 3 5 3 *Theoretical knowledge acquire from un 5 3 3 3 Not fitting in , family member ill2 2 2 2 There was a conflict with my work comit5 3 2 5 A family member that has passed away, 4 3 5 3 A combination of illness and long hours o 1 1 5 3 Finances were a big issue; Public transpo 1 2 4 3 Incompetent lecturers and tutors1 1 1 5 Thought the degree wasn't quite for me 2 2 3 4 The course never really involved the asp 3 4 3 3 I was currently working in the field and m 2 2 2 2 There was a mismatch between the stru 3 3 5 1 I found the attitude of the faculties, the 1 2 3 4 no incentive to finish degree, in relation t 4 3 4 3 Workshops/Tutorials should have a tutor 4 4 3 1 The reason I quit was that I involved in c 2 2 4 2 Definitely the commute and transport to 2 3 2 5 I feel I picked the wrong degree. I found 3 2 2 3 I lost interest in IT through the university 2 2 2 4 I didnt understand the content and I just 2 2 4 5 Doing CSSE1001, there was a lot of expe 2 2 4 3 Work commitments1 1 4 5 The degree just was not for me. Mathem 3 3 3 3 As a female, I guess it was easier to quit 1 1 1 5 Fell pregnant. I had been working full-ti
1 1 3 4 Couldn't get used to taking personal resp 1 1 1 5 It seems amazingly odd that when I, a s 1 1 1 5 Having picked the wrong degree, and als 2 2 2 4 I picked the wrong degree.1 4 3 5 That I chose the wrong degree for me, a 1 1 1 1 The course was not well structured and w 1 1 1 5 I picked the wrong degree, I am not incl
Other Comments Hours worked per week Hours worked per Open-Ended 1=0hrs, 2=0?-5,3=5-10,4=10-20, 5=20- CategoriesI did not like the pace of teaching in th 4 10-20
I felt that most of the information cove 4 10-20One of the issues that I thought was im 6 30-40
ld that this degree will not qualify me to 6 30-40 e in 2002. There are no options in the dr 1 0
Unfortunately, while obviously masters 7 40While I was at university I wasn't awar 4 10/20/2011I believe my creative abilities exceed m 4 10/20/2011The overall experience was not bad, th 4 10/20/2011
background in Physics and Software. Di 3 05/10/2011 erce 2 0-5
The reasons this PhD was not terminat 7 40No. 2 0-5
(when your scholarship isn't paid for 18 5 20-30The lack of synergy between the Facult 4 10/20/2011Quality of lecturers - it played a major 3 05/10/2011*Lecturers automatically assume you h 1 0*Hate it *Lecturers teach you on assumption you have x amount of knowledge in a certain field wh
4 10/20/2011I personally didn't enjoy studying. I pre 7 40one thing i regret doing since i felt low 2 0-5I greatly enjoyed my time at the uni, th 2 0-5The majority of my lecturers were unab 4 10/20/2011
5 20-30 from the get go. Toughed it out for 1.5 6 30-40
I believe if I had enrolled in a different 5 20-30I found it very difficult to get acknowled 6 30-40Overall, I am very happy with ICT and 4 10/20/2011Firstly, I hold UoW in the highest regar 6 30-40
to job prospects. 4 10/20/2011I already had a bcom. The new degree state no prior programming knowledge essential. I went to I cancelled my research in 2007. No ch 2 0-5It was an enjoyable degree as a whole 4 10/20/2011I had trouble organizing industry placem 4 10/20/2011
y's conception of what IT is. It was pre 1 0I felt like the whole course revolved aro 5 20-30
ectation that students had lots of prior e 4 10/20/20116 30-40
matics is not my cup of tea. t because it "just wasn't my thing". It ha 1 0
I thought I would be good at the cours 6 30-40
Sooner or later I had to learn to manag 3 05/10/2011See above. As an additional point, perh 6 30-40
so not enjoying the classes. 4 10/20/2011While I did think I'd picked the wrong d 4 10/20/2011I'm still at uni, now in a different degre 3 05/10/2011
was undergoing restructuring the year t 3 05/10/2011There was absolutely no group work in 1 0
Ranking of final grade in High School: Was degree your first If ICT degree was not r first choice,why yo 1=top 10%, 2=next 20%, 3=middle 40%, 1=yes, 2=n Degree tha Open-ended
2 1
5 11 12 13 11 0 MathematicIt was the second degree of a double degr 2 12 2 I was not accepted for the course after my 2 13 0 Mechanical It was still an engineering degree going int 2 2 I enrolled as it was a pathway to getting to 2 13 12 12 12 13 1
hen you don't3 23 13 0 mechatron since it was the other degree that i wthoug
12 13 11 12 12 1
13 12 1
o the tutorial expecting to be led through examples that could be built upon - this had worked for me in the 1 12 1 n/a2 2 Got conditional early entry. Didn't meet the 2 13 12 0 Bachelor of Teachers and guest speakers in high schoo 1 2 To learn IT/electrical engineering concepts
1 11 1
2 12 12 11 13 12 13 1
Was ICT degree the fi Did you miss the begin Undergrad Domestic/i Orientation visit? Did you att 1=yes, 2=n Name of th 1=yes, 2=n Why misse 1=UG, 2+P1=Dom, 2=1=yes, 2=n Reason 1=yes, 2=n
1 2 1 1 1 0
1 2 1 1 1 01 2 1 1 1 11 2 2 2 1 20 Bachelor of 2 1 1 1 20 BSc - Comp 2 1 1 0 I was a pas 01 2 1 1 1 11 2 1 1 1 21 2 1 1 1 01 2 1 1 1 21 2 1 1 1 00 BE (Electric 2 2 1 1 12 0 I was a litt 2 1 1 00 BCompSci, 2 2 1 0 Had been a 11 2 1 1 1 11 2 1 1 1 01 1 1 1 1 0
1 2 1 1 1 21 2 1 1 1 01 2 1 1 0 due to that 01 2 1 1 1 01 2 1 1 1 01 2 1 1 1 01 2 1 1 1 01 2 1 1 1 11 2 1 1 2 10 Bachelor of 0 Advanced s 1 1 2 21 2 1 1 1 01 2 1 1 1 2
past. Not suck it and see then we will examine you on what you don't know. If I am going to pay in 2 2 2 1 1 21 2 1 1 1 01 2 1 1 1 01 2 1 1 0 The inform 01 2 1 1 1 01 2 1 1 1 12 2 1 1 2 2
1 2 1 1 1 11 2 1 1 1 0
1 2 1 1 1 11 2 1 1 1 01 2 1 1 1 01 2 1 1 1 01 2 1 1 1 01 2 1 1 1 01 2 1 1 2 0
tend functio Performanc Marital statLanguage a English at hparents income Ethnicity Other ethnIf No, pleas 1=top 10% 1=single, 2=single wit 1=yes, 2=n1=<40,000, 2=40,000-80,000 1=arab, 2=Austrlaian, None were 3 4 English 1 3 2
there were 3 4 English 1 3 22 1 English 1 3 23 5 English 1 3 154 1 Polish 2 2 2
Clashes wit 3 5 English 1 3 23 1 English 1 2 23 1 English 1 2 2
Was not aw 3 1 English 1 1 24 1 English 1 3 2
Wasn't any 2 1 English 1 3 33 1 English 1 3 15 Switzerland
I felt insecu 3 1 English 1 1 22 1 English 1 3 22 1 English 1 3 2
The distanc 2 1 Cantonese 2 3 4Only functi 3 1 English and 1 2 1
4 1 English, Ta 1 1 15 Born Austra Work comm 5 1 english 1 3 2felt that i w 4 1 Indonesian 2 2 8A combinat 3 1 english 1 1 2Work comm 4 1 English 1 1 2Nothing wo 3 1 English 1 2 2Probably co 3 1 English (Au 1 3 2
3 1 English 1 3 23 4 English 1 2 21 3 English 1 2 2
I was not i 3 1 English 1 3 24 1 english 1 2 2
n excess of $1K per subject I expect some degree of teaching to happen in workshops/tutorials.2 5 mandarin 2 1 4
I left befor 2 1 Persian (fa 2 1 15 AfghanWasn't awa 2 1 English 1 2 2Wasn't inte 3 1 Mandarin 2 3 4I don't thin 3 1 english 1 3 2
3 1 English 1 2 22 4 English 1 3 2
2 1 English 1 1 2I don't rem 1 6 English 1 1 2
5 1 English; or 1 2 2 Chinese...You actu 3 1 English 1 1 2I'm not a s 4 1 English 1 3 2I wasn't int 1 1 English 1 3 2There were 5 1 English 1 2 2Not interes 2 1 English 1 3 2There were 5 1 English 1 2 2
52 1222178504 1 Under 18 1 453 1222178388 1 Under 18 1 454 1222147900 1 Under 18 1 455 1222139783 1 Under 18 1 456 1222077815 1 Under 18 2 457 1222044916 1 Under 18 2 458 1222042412 1 Under 18 1 459 1222037253 5 21 2 460 1222014062 11 36-45 2 461 1222012382 4 20 1 662 1218327696 3 19 2 863 1216986245 7 23 2 564 1208458386 2 18 2 165 1206901106 10 26-35 2 866 1205460139 11 36-45 2 567 1203051242 8 24 2 468 1201999407 2 18 1 469 1199456611 6 22 2 470 1199362631 2 18 2 171 1199297888 3 19 2 472 1198576126 10 26-35 2 273 1197984506 11 36-45 2 174 1196663007 10 26-35 2 475 1195427148 10 26-35 2 476 1195341442 11 36-45 1 577 1195070227 9 25 2 178 1194952664 11 36-45 2 479 1194906729 7 23 2 580 1194836343 11 36-45 2 181 1194529109 9 25 2 182 1194476557 2 18 2 583 1194376598 11 36-45 2 184 1193695407 10 26-35 2 485 1193602296 9 25 2 486 1193396953 5 21 2 687 1193393433 2 18 2 488 1192651509 4 20 2 189 1192318318 7 23 2 590 1192248104 10 26-35 2 791 1192124970 10 26-35 2 192 1192034447 10 26-35 2 193 1192013626 2 18 2 594 1191806196 2 18 1 495 1191798664 2 18 1 496 1191772021 2 18 1 497 1191427436 2 18 2 198 1191170731 1 Under 18 2 199 1191051378 3 19 2 4
100 1191029072 6 22 2 5101 1191024586 2 18 2 4102 1191015416 2 18 2 6103 1190956136 3 19 1 4104 1190934701 6 22 2 8
1 Between 1 February an 1 3 214 Between 1 and 28 Feb 1 2 313 Enrolled but didn't beg 1 3 414 Between 1 and 28 Feb 1 3 424 Between 1 and 31 Dec 2 2 34 Between 1 February an 1 3 2
30 Between 1 and 28 Feb 1 1 218 Between 1 and 30 June 1 3 523 Between 1 and 30 Nov 1 3 42 Between 1 July and 31 1 3 4
Professional Software De 34 Between 1 and 30 June 1 2 27 Between 1 February an 1 1 2
30 Between 1 and 28 Feb 1 1 2Mathematics and Financ 1 Between 1 February an 1 3 3
19 Between 1 and 31 July 2 1 114 Between 1 and 28 Feb 1 1 532 Between 1 and 30 Apri 1 3 330 Between 1 and 28 Feb 1 3 111 Between 1 July and 31 1 3 32 Between 1 July and 31 1 2 3
24 Between 1 and 31 Dec 1 3 38 Between 1 July and 31 2 1 2
10 Between 1 February an 2 3 1project management 13 Enrolled but didn't beg 2 1 1
32 Between 1 and 30 Apri 2 3 111 Between 1 July and 31 1 2 55 Between 1 July and 31 2 3 24 Between 1 February an 1 1 38 Between 1 July and 31 1 3 3
Games Technology 4 Between 1 February an 1 1 28 Between 1 July and 31 1 1 4
Master Science of IT 11 Between 1 July and 31 1 1 330 Between 1 and 28 Feb 2 1 28 Between 1 July and 31 1 1 2
19 Between 1 and 31 July 1 1 215 Between 1 and 31 Mar 1 2 24 Between 1 February an 1 3 4
32 Between 1 and 30 Apri 1 2 510 Between 1 February an 2 3 231 Between 1 and 31 Mar 2 1 111 Between 1 July and 31 2 2 5
To be more specific, Bus 11 Between 1 July and 31 1 3 1Network Design and Sec 22 Between 1 and 31 Octo 1 3 1Network Design and Sec 22 Between 1 and 31 Octo 1 3 2
18 Between 1 and 30 June 1 3 21 Between 1 February an 1 3 2
34 Between 1 and 30 June 1 3 311 Between 1 July and 31 1 3 518 Between 1 and 30 June 2 2 47 Between 1 February an 1 3 2
18 Between 1 and 30 June 1 2 3multimedia 14 Between 1 and 28 Feb 1 1 4Masters in IT (already h 1 Between 1 February an 2 2 2
2 2 2 2 4 2 2 4 44 1 2 3 3 2 3 4 51 1 3 2 2 2 4 4 41 4 4 2 3 3 4 5 41 1 1 1 4 1 2 2 42 2 2 2 2 4 4 2 41 3 4 5 3 1 2 2 32 3 2 2 2 2 5 2 21 4 2 2 2 2 3 3 22 2 2 2 2 2 2 2 51 1 1 1 1 1 1 4 22 2 2 2 2 2 4 2 22 5 1 1 2 2 4 2 42 2 3 3 2 2 5 2 21 1 1 1 1 1 5 1 14 4 5 5 4 4 4 4 33 1 2 2 1 3 3 3 31 1 1 1 1 1 1 11 1 2 2 1 1 4 1 23 4 5 4 5 4 3 5 42 3 4 3 3 3 4 4 33 3 2 2 3 2 3 2 21 3 1 1 1 1 1 1 11 4 2 2 2 2 4 4 32 2 1 1 2 1 3 2 21 5 4 3 5 5 4 5 22 2 3 2 2 3 4 2 32 2 2 3 3 3 2 3 43 5 5 3 3 4 1 1 52 5 3 2 2 2 3 2 23 4 3 2 5 2 4 2 32 2 3 2 2 2 2 3 21 1 1 1 1 1 1 1 13 1 1 1 2 2 4 3 22 2 2 4 2 2 2 2 22 2 2 2 2 2 2 2 21 4 3 3 3 3 4 4 53 3 3 3 3 4 5 1 42 5 3 2 3 3 3 3 31 1 1 1 1 1 4 1 13 3 3 3 3 2 2 1 53 4 1 1 1 5 4 5 23 2 2 2 1 2 2 3 33 2 2 2 1 1 2 3 34 4 2 2 3 1 2 2 21 1 1 1 3 2 2 5 52 3 2 2 2 2 2 4 21 1 2 3 4 1 3 3 51 5 2 3 3 2 4 2 43 4 4 1 1 1 4 4 41 2 2 2 3 2 4 5 44 2 4 2 2 3 4 3 42 3 2 2 4 1 2 3
2 4 2 2 2 2 2 2 41 4 4 1 1 4 1 1 32 3 1 3 1 3 2 3 33 4 2 4 3 3 4 2 22 4 3 1 2 2 3 4 22 2 2 2 2 2 2 2 22 2 2 1 2 4 2 3 22 2 4 2 2 2 2 2 23 2 2 2 3 2 2 2 22 2 2 2 2 2 2 2 21 2 1 2 2 2 2 2 22 2 2 2 2 2 2 2 24 4 4 3 2 2 2 2 22 3 3 2 2 3 3 3 31 1 1 1 1 1 1 1 14 2 3 3 4 4 3 4 43 1 3 3 3 3 3 3 31 1 1 1 1 1 1 1 11 2 4 1 3 2 1 22 5 4 3 2 3 2 4 34 4 2 4 3 3 4 3 41 1 2 1 2 2 2 2 11 1 1 1 1 1 1 1 12 2 2 2 2 2 2 2 32 2 2 2 2 2 2 2 22 5 2 1 4 4 5 3 22 3 3 2 2 3 3 2 23 3 3 3 3 2 3 3 44 5 5 3 3 4 3 3 42 3 3 3 2 2 3 3 42 2 2 3 2 2 3 2 42 3 3 3 3 3 2 2 21 1 1 1 1 1 1 1 12 1 2 1 1 1 1 3 32 2 2 2 2 2 2 2 32 2 2 2 2 2 2 2 23 3 4 3 2 4 3 3 43 4 4 3 5 3 5 1 33 2 3 2 3 5 3 3 31 1 1 1 1 1 1 1 13 3 3 3 3 4 3 3 21 4 1 5 2 4 3 3 42 2 2 4 2 2 2 2 32 2 2 4 2 22 2 2 4 1 2 2 2 22 4 2 3 2 2 2 1 42 2 2 2 2 2 2 4 42 4 2 1 2 4 5 2 34 4 2 2 4 4 4 2 15 4 4 2 1 2 3 2 44 4 2 3 2 3 2 3 42 3 2 3 2 3 2 3 43 3 4 2 2 2 2 2 5
4 4 4 5 4 2 3 5 23 1 4 2 4 2 3 4 25 3 4 2 4 4 3 4 25 4 3 2 3 5 4 5 45 2 5 5 3 5 2 5 32 4 2 4 2 2 2 2 23 2 3 2 2 4 1 2 22 2 4 4 4 2 2 4 22 2 2 1 2 2 3 3 35 4 2 2 2 4 2 2 43 2 2 2 2 2 2 5 22 2 2 2 2 2 2 2 22 2 5 5 54 3 3 3 3 3 3 4 31 1 1 1 1 1 1 1 13 4 4 4 4 3 3 2 23 3 3 3 3 3 3 3 31 1 1 1 1 1 1 1 12 4 3 4 1 2 2 14 1 5 5 2 5 5 4 44 2 3 3 3 3 3 3 42 1 3 3 3 2 2 2 21 1 1 1 1 1 1 3 14 3 4 2 4 4 2 3 22 2 2 2 2 2 2 2 22 4 4 2 3 2 5 5 44 2 3 2 3 4 3 4 43 3 4 3 4 3 3 4 31 3 3 3 3 1 5 5 32 2 2 2 3 2 2 2 22 3 4 3 4 2 2 4 24 2 2 2 2 3 2 3 31 1 1 1 1 1 1 1 13 2 2 2 2 3 1 1 22 1 2 2 2 2 2 2 22 2 2 2 2 2 2 4 21 4 2 2 3 1 3 4 11 3 3 3 3 1 5 5 13 3 3 3 3 3 3 3 31 1 1 1 1 1 1 1 11 2 3 2 3 1 4 5 15 2 5 4 4 5 3 5 44 3 3 3 3 4 2 4 4
2 2 2 2 2 2 2 1 15 2 1 4 1 4 1 1 43 2 3 2 3 4 2 3 34 4 2 2 2 1 2 3 21 2 5 4 5 2 2 2 14 2 1 2 2 1 4 5 14 4 4 4 4 5 3 3 44 4 4 3 4 4 3 4 22 2 5 5 5 1 5 5 2
1 2 2 1 3 5 5 2 51 3 2 1 2 1 3 1 42 4 4 3 2 4 4 2 41 3 4 1 4 3 4 2 21 3 1 3 5 5 1 32 2 2 2 2 2 2 2 22 2 4 2 4 4 3 2 52 2 2 2 2 2 2 2 22 4 3 2 3 3 3 1 42 2 2 2 2 2 4 2 21 4 2 1 2 3 3 2 22 2 2 2 2 2 2 2 2
2 3 3 2 3 4 2 3 31 1 1 1 1 1 1 1 13 2 3 3 2 2 4 3 33 3 5 3 3 3 3 3 31 1 1 1 1 1 1 1 11 1 4 1 3 1 1 1 11 4 5 1 1 5 5 3 23 3 4 3 3 3 3 3 41 2 2 1 2 2 2 2 21 1 3 1 1 1 1 1 12 4 3 2 4 4 3 2 42 2 2 3 2 2 2 2 21 4 3 1 4 3 3 1 31 2 4 1 2 2 2 2 23 3 3 3 3 4 3 3 33 3 3 3 3 3 3 3 32 2 3 2 4 2 2 2 13 4 2 2 4 4 3 3 52 2 3 1 2 2 2 3 21 1 1 1 1 1 1 1 13 4 3 1 1 2 1 3 22 2 2 2 2 2 2 2 42 2 2 2 2 2 3 2 21 3 4 1 4 2 4 3 44 3 1 3 4 5 4 3 33 4 3 3 3 3 3 3 31 1 1 1 1 1 1 1 14 4 2 3 3 4 3 3 31 5 5 5 4 5 4 1 52 2 3 3 3 2 4 2 1
1 2 2 5 2 5 5 41 2 2 1 3 4 4 1 42 2 3 2 4 2 3 2 21 1 2 1 1 4 5 1 52 2 1 2 2 2 5 2 21 4 2 1 1 2 4 1 22 4 5 2 2 4 4 2 32 4 4 2 2 2 2 2 21 2 1 1 3 1 3 1 1
1 4 4 1 2 1 2 4 41 3 4 1 3 3 3 3 52 4 4 2 2 1 1 1 11 4 4 2 2 1 2 3 51 2 5 1 1 1 1 1 12 2 2 2 2 2 2 2 22 2 4 2 2 1 2 2 22 2 2 2 5 3 3 5 31 4 4 1 4 3 3 42 2 3 2 2 2 2 2 21 2 4 1 1 2 2 1 32 2 2 2 2 2 2 2 2
2 3 3 3 3 3 2 2 31 1 1 1 1 5 1 1 13 3 3 3 3 3 3 3 33 3 3 3 3 3 3 3 11 1 1 1 1 1 1 1 11 1 1 1 3 1 1 1 11 5 5 1 4 1 1 3 13 3 3 3 2 2 2 2 21 2 2 2 5 1 3 3 31 1 1 1 5 1 1 1 12 5 3 2 5 4 3 3 42 2 2 2 2 2 3 3 31 1 1 1 3 3 3 3 31 1 4 1 1 1 1 1 13 3 3 3 2 2 2 3 33 3 3 3 3 1 4 1 42 2 4 2 5 1 5 1 52 4 2 2 4 2 3 2 21 2 2 2 2 1 1 1 11 1 1 1 1 1 1 1 13 3 2 3 1 1 1 1 12 2 4 2 2 2 2 22 2 2 2 2 2 2 2 21 1 4 3 5 4 3 3 43 5 3 3 3 3 3 3 33 3 3 3 4 3 3 3 31 1 1 1 5 1 1 1 13 3 3 3 3 3 3 3 34 5 5 1 4 5 1 1 12 4 5 1 2 1 2 3 2
5 5 5 4 2 2 2 2 41 1 1 1 1 1 1 2 42 2 4 2 3 3 3 3 41 1 4 1 1 4 1 1 11 1 3 2 4 2 2 2 21 2 2 1 1 4 4 3 42 2 5 2 1 1 4 1 22 2 4 2 4 4 2 2 21 1 3 1 1 1 1 1 1
2 5 1 1 4 1 1 5 13 5 3 3 2 4 4 1 11 1 1 1 4 2 4 1 13 5 1 1 3 1 3 1 11 2 1 1 1 1 1 1 12 2 2 2 2 2 2 2 22 2 1 1 2 1 4 2 13 3 3 3 3 5 3 5 33 4 3 3 2 4 3 3 32 2 2 2 2 2 2 2 24 2 1 1 1 1 1 1 12 2 2 2 2 2 2 2 2
3 4 2 1 4 3 3 2 31 1 1 5 1 1 5 1 13 3 3 3 3 3 3 3 33 1 3 3 3 3 1 3 31 1 1 1 1 1 1 1 11 1 1 1 1 4 1 1 13 2 1 1 3 2 1 1 12 2 2 2 3 3 3 3 33 2 1 1 3 5 1 3 11 1 1 1 1 5 1 1 13 4 1 1 1 5 3 5 33 3 1 2 2 2 2 2 23 3 3 3 3 3 3 3 31 1 1 1 1 2 1 1 12 3 2 2 3 3 3 3 33 4 3 3 1 1 1 3 11 5 1 1 1 5 1 4 13 1 3 3 2 5 3 4 31 1 1 1 2 1 4 5 11 1 1 1 1 4 1 1 11 1 1 12 2 2 2 2 2 2 2 42 2 2 2 2 2 2 2 23 4 3 1 3 4 1 4 13 3 3 3 3 3 3 3 33 3 3 3 3 4 3 3 33 1 1 1 1 5 1 1 13 3 1 1 3 1 1 3 11 1 1 4 1 5 1 1 53 2 1 3 2 2 2 3 1
3 4 2 2 2 2 2 2 23 4 1 1 1 1 1 1 13 4 3 3 2 2 3 2 21 1 1 1 1 1 1 1 12 2 2 2 2 4 2 2 23 5 1 1 2 3 4 3 22 4 1 1 2 2 1 4 13 2 2 2 2 4 3 3 13 1 1 1 2 1 1 2 1
1 4 4 5 The degree I chose wasn't the right one 1 3 5 4 I had been doing ITS (Information Techn 1 1 1 4 I was not interested in the degree and w 1 1 1 5 I had too much trouble with the course w 1 1 1 4 Never intended on finishing the degree i 2 2 2 4 i picked the wrong degree1 2 2 2 Despite being interested in the course co 3 3 5 3 Possibly chose the one as I wanted to fo 3 3 2 4 transferred to BEng2 2 2 4 My strengths lie far from those necessary 1 1 1 4 Course content no longer interested me. 2 2 2 2 For immigration
3 3 4 4 Personal interest in the coursework. I am 1 1 1 1 Personal reasons - I would have remaine 3 2 3 2 BECAUSE ANOTHER COUSE WILL BE BET 3 3 3 3 Not enought help from staff was offered1 1 1 1 Because I wanted to be a network engin1 1 1 1 I felt some of the teaching staff were no 1 2 1 3 The course was far too technical. The ye 2 3 4 2 Too much memorise more than the real 1 3 3 2 Conflict with my work commitments. Wit 1 1 3 3 I left the degree because there was no o 2 3 5 3 The reason I havenot continued with my 4 2 2 4 Picking the wrong degree. I chose the d 3 3 3 2 The course material/teachers not being a1 1 2 3 You needed the Cisco CCNA in order to d 3 3 3 3 other course have more opportuniy to ap 2 1 5 1 none as they dont address the main issu 1 1 5 1 My housemates skipped out on me while 3 3 4 2 Working and affording to live without im 1 3 5 1 I left my degree due to a financal proble 1 1 1 1 Decided to graduate with Postgraduate D
none2 2 4 2 kdkdkdk2 2 2 4 The course did not meet my expectation 1 3 4 4 Finding work and at the same time realis 3 3 3 3 Swinburne was an academic farce.3 3 3 3 Working full time, its too hard to attend 1 1 1 1 Work commitments were too high.1 3 5 3 The lack of RPL was a big problem for m 1 5 1 5 The degree wasn't right for me - too mu 1 2 3 5 It wasn't for me from the start and I fou
2 2 2 4 Got married1 1 4 5 I found the degree I chose to be far too 2 3 2 4 N/A1 1 1 5 I was not enjoying IT full stop. It was un 2 2 2 4 I changed to an Online degree due to it's 1 3 5 4 There were more personal reasons, but t 1 2 3 5 I picked the wrong degree. I didn't have 3 3 4 5 I was having personal problems at the ti 1 1 4 2 Course content is not relevant to real wo
for me. Some of the content seemed ir 3 05/10/2011I found it to be very code focused, and 4 10/20/2011As a female it was quite daunting being 4 10/20/2011
work, and there wasnt enough help ava 4 10/20/2011The experience wasn't bad I just wante 4 10/20/2011
I will be returning to my IT degree nex 2 0-5UQ could do with some more web deve 7 40no 2 0-5N/A 2 0-5The staff and students in the course we 1 0
1 0
m way more interested in my current de 5 20-30 ed in study otherwise 3 05/10/2011
NO THANKS 5 20-30None 1 0
eer 3 05/10/2011Too much of an emphasis on game app 4 10/20/2011Studying COBOL programming? Create 3 05/10/2011
understand the lesson. 4 10/20/2011Some of the questions in the survey we 6 30-40Murdoch needs to have a good online learning facility. In this day and age it is important people ca I guess there needs to be a degree for 4 10/20/2011I felt my tutors were great and very pa 7 40I would call my experience studying Co 4 10/20/2011No, all that I have said is written above 1 0no comment 4 10/20/2011i came through tafe and wated my time 1 0I original started university as a midyea 5 20-30
pacting performance while studying 7 40Some lecturers did not have enough fo 2 0-5
Diploma Degree 6 30-40i didn't actually leave my degree. i defe 1 0
2 0-5 s and due to a lack of interest, I withdre 4 10/20/2011
sing that I found CS boring (wrong deg 5 20-30To be honest, I believe it was my own 3 05/10/2011Have evening classes for Part Time stu 6 30-40
7 40To be clear, RPL means Recognition of 7 40I think my time management and overa 4 10/20/2011I find all the questions in this survey di 4 10/20/2011
Degree simply wasn't what I wanted. R 3 05/10/2011Since leaving Murdoch I completed a D 5 20-30N/AWhere was my special help? Where wa 3 05/10/2011
s flexibility and offer of more business r 6 30-40 the content of the course was not what 1 0
It was a combination of the fast moving 3 05/10/2011 me and couldnt do both. I also dropped 5 20-30
I found the IT Masters @ Murdoch to b 7 40
1 12 12 11 13 2 TO get a higher GPA
2 0 Bachelor of Head that it was related and simpler to tra 2 12 0 beng did not meet prereqs1 2 I changed from a BA to Multimedia Design 1 12 1
3 12 13 12 11 23 12 1 N/a2 12 1
an study off campus because it is becoming harder to live without full time work.1 0 I wanted to It was my first choice but I wanted to mov 3 22 12 13 13 13 12 14 12 13 11 2 Didn't like engineering3 12 11 13 22 13 12 1 I think I had Monash's IT degree as first pr 2 1
2 13 0 Video Gam I thought I may have been able to pass a y
2 2 My friend was in the year above in the cou 2 11 11 12 0 Electronic BI wanted to do Electronic business but they 1 1
1 2 1 1 1 01 2 1 1 1 11 2 1 1 1 21 2 1 1 1 02 2 1 1 1 2
1 2 1 1 1 11 2 1 1 1 21 2 1 1 1 00 BA 2 1 1 1 01 2 1 1 1 11 2 1 2 1 1
0 BMathFin 2 1 1 1 01 2 1 1 2 22 2 1 2 2 11 2 1 1 1 10 Bachelor of 2 1 2 1 11 2 1 1 1 11 2 1 1 1 11 2 2 2 1 11 2 1 1 0 Full time w 0
0 Bachelor O 2 1 1 0 I was work 01 2 1 1 0 Work and f 01 2 1 1 0 I went to t 01 2 1 1 1 22 2 1 2 1 11 2 1 1 1 11 2 1 1 0 I was work 21 2 1 1 1 21 2 2 2 2 01 2 2 1 2 22 2 2 2 2 10 Engineering 2 1 1 1 21 2 1 1 1 11 2 1 1 1 10 Bachelor of 2 1 1 1 11 2 2 1 0 During nor 20 Bachelor of 2 1 1 1 01 2 1 1 1 11 2 1 1 1 11 2 1 1 1 2
1 2 1 1 0 Waste of ti 11 2 1 1 1 1
1 2 1 1 1 02 2 1 1 0 During wor 01 2 1 1 1 11 2 1 1 1 11 2 1 1 1 00 Bsc Compu 2 2 1 0 I had alrea 0
Didn't know 5 4 English 1 2 22 1 English 1 3 25 1 English 1 2 2
There were 4 1 English 1 2 23 1 English 1 3 15 Venezuela
5 1 English 1 1 24 1 englsh 1 1 15 Aboriginal
timings 3 5 English 1 1 2No interest 3 1 English 1 3 2
2 1 English 1 3 23 1 Chinese 2 2 4
I don't rem 4 1 English 1 2 21 1 Jibberish 1 3 22 1 cantonese 2 1 43 1 English and 1 3 73 1 Sinhala 2 2 15 Sri Lanka3 1 English 1 3 24 1 ENGLISH 1 3 24 4 Thai 2 1 13
Full time w 2 5 English 1 2 2
No time, th 4 4 English 1 1 2Work and f 3 2 English 1 3 2Was not aw 3 1 English 1 3 15 New Zealan
2 1 english 1 1 33 1 vietnamese 2 1 143 5 english 1 1 23 1 English 1 1 22 4 English 1 2 2
I thought, 4 5 Korean 2 2 15 Korea3 1 Indonesian 2 2 83 1 English 1 2 112 1 English 1 3 22 1 English 1 3 22 1 English 1 2 21 1 English 1 3 21 5 English 1 2 4
Wasn't awa 5 4 English 1 3 21 1 English 1 3 24 1 English 1 3 2 Jewish3 1 English 1 1 2
2 1 English 1 2 15 South Afric5 4 English 1 3 2
I started of 5 1 English. 1 2 2None to att 2 1 English 1 3 2
3 1 English 1 3 24 1 English 1 2 2
Wasn't rea 3 1 english 1 2 2Functions? 2 4 English 1 3 2
105 1190912145 2 18 2 4106 1190879121 2 18 2 5107 1190877246 2 18 1 5108 1190623677 11 36-45 1 4109 1190041454 8 24 2 5110 1190041001 11 36-45 2 4111 1189985089 11 36-45 2 1112 1189919452 6 22 2 1113 1189912822 10 26-35 1 5114 1189900504 1 Under 18 2 7115 1189875191 3 19 2 4116 1189870321 10 26-35 2 1117 1189850026 1 Under 18 2 8118 1189847214 11 36-45 2 4119 1189827848 1 Under 18 2 1120 1189827228 2 18 2 5121 1189809374 11 36-45 2 4122 1189799022 7 23 2 5123 1189789613 3 19 2 1124 1189787480 11 36-45 2 1125 1189770802 9 25 2 5126 1189760756 10 26-35 2 8127 1189759806 8 24 2 1128 1189753820 9 25 2 1129 1189751792 5 21 2 1130 1189749012 1 Under 18 2 8131 1189735420 10 26-35 2 1132 1189735415 8 24 1 1133 1189732889 5 21 2 8134 1189727698 10 26-35 2 4135 1189714367 3 19 2 8136 1189713787 2 18 2 1137 1189707735 10 26-35 2 1138 1189698866 5 21 2 1139 1189697395 2 18 1 1140 1189693883 1 Under 18 2 8141 1189692645 2 18 1 1142 1189683930 3 19 2 1143 1189683613 10 26-35 2 1144 1189683437 10 26-35 2 1145 1189683121 7 23 2 1146 1189682427 1 Under 18 2 1147 1189679218 11 36-45 1 4148 1189678159 1 Under 18 2 8149 1189649088 11 36-45 2 4150 1189645459 10 26-35 2 4151 1189621001 11 36-45 2 8152 1189617049 3 19 2 4153 1189608102 10 26-35 1 4154 1189597846 8 24 2 1
2 Between 1 July and 31 1 213 Enrolled but didn't beg 1 3 413 Enrolled but didn't beg 1 3 58 Between 1 July and 31 2 3 21 Between 1 February an 1 3 3
10 Between 1 February an 2 3 28 Between 1 July and 31 2 3 4
Internetworking security 23 Between 1 and 30 Nov 1 3 41 Between 1 February an 2 3 22 Between 1 July and 31 1 1 25 Between 1 July and 31 2 1 1
10 Between 1 February an 2 3 1Games software design 17 Between 1 and 31May 1 2 5
11 Between 1 July and 31 2 1 118 Between 1 and 30 June 1 2 410 Between 1 February an 1 1 18 Between 1 July and 31 2 3 3
14 Between 1 and 28 Feb 1 3 522 Between 1 and 31 Octo 1 3 210 Between 1 February an 2 1 38 Between 1 July and 31 2 3 4
Games Technology 8 Between 1 July and 31 1 3 414 Between 1 and 28 Feb 1 3 35 Between 1 July and 31 1 1 1
Networking and Security 7 Between 1 February an 1 1 4Internetwork and Securi 4 Between 1 February an 1 1 4computer security 11 Between 1 July and 31 1 3 2
1 Between 1 February an 1 3 2Business Information Sy 8 Between 1 July and 31 1 1 4Networking administratio 14 Between 1 and 28 Feb 1 1 1double major computer 20 Between 1 and 31 Aug 1 1 3
1 Between 1 February an 1 3 534 Between 1 and 30 June 1 1 211 Between 1 July and 31 1 3 41 Between 1 February an 2 3 5
Games Technology 11 Between 1 July and 31 1 2 224 Between 1 and 31 Dec 1 3 41 Between 1 February an 1 2 37 Between 1 February an 2 1 2
Internet Science 1 Between 1 February an 2 1 311 Between 1 July and 31 2 1 211 Between 1 July and 31 1 2 41 Between 1 February an 2 1 1
Internetworking & Secur 10 Between 1 February an 1 2 332 Between 1 and 30 Apri 1 3 211 Between 1 July and 31 1 3 3
Games Software Design 11 Between 1 July and 31 2 1 25 Between 1 July and 31 1 3 28 Between 1 July and 31 2 2 12 Between 1 July and 31 2 1 2
1 2 4 2 2 1 4 2 41 3 4 4 2 2 2 2 31 4 4 4 2 2 2 2 21 3 1 2 3 2 1 4 21 4 5 4 5 2 3 4 52 2 1 1 1 2 2 2 2
4 1 11 2 3 4 5 2 1 4 53 2 4 3 4 3 4 3 21 2 1 1 2 1 3 1 11 3 1 1 2 1 1 2 21 1 1 1 1 1 1 1 31 2 4 3 4 2 3 4 32 4 1 1 2 1 2 1 11 2 2 1 2 2 2 1 21 1 1 1 1 1 1 1 11 3 3 1 3 3 2 3 32 2 4 4 2 5 3 2 42 4 2 3 2 4 4 4 42 5 4 2 2 2 3 3 23 2 2 2 3 2 4 2 42 4 3 2 3 3 3 4 42 4 3 2 1 3 3 3 42 4 2 2 2 1 2 2 31 3 2 3 4 3 3 3 21 3 1 1 1 1 3 2 42 2 2 2 2 2 4 2 22 2 5 1 3 3 2 5 31 1 1 1 1 1 1 1 23 1 2 1 3 2 3 5 32 3 4 3 2 2 5 5 51 4 4 3 3 5 32 2 2 2 2 2 2 2 23 4 2 4 2 2 4 4 43 4 4 3 3 3 3 1 51 1 1 3 4 2 2 2 52 2 2 3 4 2 2 4 52 2 5 5 2 4 2 2 32 2 2 2 2 2 22 2 3 3 3 3 4 2 43 2 2 2 3 2 5 2 23 3 3 4 3 2 3 4 51 1 1 1 1 1 1 1 12 2 2 3 4 2 2 2 22 2 2 2 3 2 5 2 21 1 2 1 2 4 5 4 22 2 2 2 3 3 3 2 21 2 2 3 2 2 4 1 31 1 1 4 3 3 1 1 52 2 2 2 2 2 4 2 2
3 4 2 5 2 4 3 4 53 2 2 4 2 2 4 4 23 2 2 2 2 2 4 4 22 3 3 1 2 2 3 1 53 3 5 3 4 4 5 4 52 2 2 2 2 1 1 2 23 2 3 3 3 2 3 3 13 4 2 2 2 3 4 5 43 5 3 2 4 4 5 3 31 1 3 1 1 1 1 1 11 2 1 1 1 1 2 2 21 1 3 1 2 2 1 1 44 4 2 3 3 5 3 3 41 1 1 1 1 1 1 1 12 2 2 2 2 2 4 2 31 1 1 1 1 1 1 1 13 3 3 3 3 3 3 3 33 3 4 2 2 4 2 2 42 2 2 4 2 2 2 4 32 2 2 2 2 2 2 2 22 2 2 2 2 2 2 2 24 2 3 3 2 2 2 3 21 4 4 2 3 2 2 1 32 2 2 2 2 2 2 2 22 2 2 3 2 3 3 2 31 3 4 3 2 3 3 2 32 2 2 2 2 2 2 2 23 4 2 5 2 4 3 2 41 1 1 1 1 1 1 1 11 2 3 1 1 1 3 4 34 5 3 1 3 5 2 3 23 2 1 3 3 3 4 4 22 2 2 2 2 2 2 2 42 2 3 4 4 4 2 23 3 3 3 3 3 4 3 52 3 2 3 2 3 2 2 33 4 3 4 3 3 3 3 34 4 4 4 4 5 5 2 3
2 2 22 3 3 3 3 3 4 3 22 2 3 2 2 2 3 2 25 5 5 3 5 5 5 3 31 1 1 1 1 1 1 1 12 2 3 2 2 2 2 2 22 2 2 2 2 2 2 2 42 2 2 3 3 5 3 4 21 1 1 3 1 1 2 2 21 2 4 3 2 1 2 3 23 3 3 3 5 5 1 1 12 2 2 2 2 2 2 2 2
3 2 2 5 2 2 4 4 22 1 1 2 1 1 2 2 22 1 1 2 1 1 2 2 25 5 4 4 4 1 3 3 43 2 3 5 5 4 4 4 32 2 2 2 2 2 2 2 21 2 3 3 3 1 2 4 15 2 2 2 2 5 2 4 23 2 2 4 3 3 4 4 31 3 1 1 12 2 2 1 2 2 1 2 21 1 4 4 4 1 3 5 14 2 4 2 4 5 3 5 41 1 1 1 1 1 1 1 12 2 5 4 4 1 2 2 21 1 1 1 1 1 13 3 3 3 3 3 3 32 2 4 5 4 2 4 5 24 3 2 2 2 2 2 4 42 2 3 2 3 2 2 2 22 2 3 3 3 2 2 4 24 2 3 3 4 4 3 4 42 2 1 2 4 2 2 5 22 2 2 2 2 2 2 2 24 3 4 3 3 3 3 3 31 3 2 2 2 1 1 1 12 2 2 2 2 4 2 2 44 5 4 4 5 4 4 4 41 2 2 2 2 1 1 4 14 5 3 4 3 5 3 5 45 2 4 1 3 5 3 5 35 5 3 1 3 5 5 5 42 2 4 2 2 2 2 2 22 4 3 4 5 2 4 4 23 3 5 5 5 1 3 5 32 2 2 3 2 2 3 4 24 3 3 3 3 4 3 3 33 4 2 5 4 1 5 5 22 2 2 2 2 2 2 2 22 2 3 3 3 2 2 3 22 2 2 2 4 1 2 3 21 3 5 5 5 2 4 3 31 1 1 1 1 1 1 1 12 2 2 2 2 2 2 2 22 2 2 2 2 2 2 4 21 2 2 1 2 2 4 4 32 2 3 2 2 3 1 2 21 3 4 5 4 1 4 5 11 1 1 5 5 1 5 5 12 2 2 2 2 2 2 2 2
2 3 3 3 4 2 4 4 21 3 2 1 1 4 4 4 31 4 2 2 21 2 5 1 1 1 3 1 31 4 3 2 4 5 5 4 32 2 2 1 2 1 2 1 2
3 3 3 3 3 3 3 34 2 4 2 5 5 5 4 52 3 3 3 3 3 3 3 3
1 2 4 1 2 2 2 2 41 1 1 1 3 1 1 1 21 3 3 2 5 4 5 1 51 1 1 1 1 1 1 1 11 2 2 1 2 2 4 2 2
1 1 13 3 3 3 3 3 3 3 32 3 3 2 2 4 4 2 23 4 4 2 3 4 3 2 22 2 2 2 2 3 2 2 22 3 3 2 2 2 4 2 22 4 3 3 2 4 4 2 42 4 2 3 3 2 2 2 42 2 3 2 4 2 1 2 11 2 3 1 5 1 3 1 31 2 3 1 2 1 4 1 12 2 2 2 2 2 2 2 21 3 4 1 3 4 3 4 41 1 1 1 1 1 4 1 43 2 3 3 3 4 3 3 41 3 5 1 5 5 4 1 32 1 5 2 5 5 5 3 52 2 2 2 2 2 2 2 22 2 4 2 4 4 4 24 3 3 3 3 4 4 3 32 3 2 2 4 5 4 2 53 4 3 3 3 5 4 3 42 1 4 4 4 2 5 3 22 2 2 2 2 2 2 2 22 2 3 2 3 4 4 2 44 2 3 2 2 3 2 24 3 3 3 3 3 5 3 31 1 2 1 1 1 1 1 32 2 2 2 2 2 4 2 32 2 2 2 3 2 2 2 33 3 4 4 3 4 5 4 53 2 2 2 1 2 2 2 24 2 2 1 2 1 1 1 21 1 1 1 1 1 1 1 12 2 2 2 2 2 2 2 2
3 2 5 2 4 3 1 2 21 4 4 1 2 3 2 1 5
1 1 3 1 5 1 1 3 11 5 4 2 4 4 3 2 32 1 2 2 2 2 2 1 13 3 3 3 2 3 3 3 32 5 5 4 2 2 4 4 23 3 3 3 4 2 3 3 4
2 2 2 2 5 3 2 3 41 5 5 1 1 1 1 1 11 2 4 1 1 1 1 4 11 1 4 1 5 3 1 3 11 2 5 2 4 3 2 2 413 3 3 32 2 2 2 4 2 2 2 22 2 4 2 2 2 2 2 22 2 3 2 5 2 3 2 52 2 2 2 2 2 2 2 22 4 5 3 4 3 4 3 22 2 4 2 5 2 2 5 21 2 2 1 4 4 2 21 1 5 1 1 1 1 1 11 1 2 1 1 1 1 1 12 2 2 2 2 2 2 2 21 4 4 2 3 2 4 1 21 1 3 1 1 1 1 1 13 3 3 3 3 3 3 3 31 1 4 1 3 3 3 1 12 5 5 3 4 3 4 4 22 2 2 2 2 2 2 2 22 4 4 2 5 5 3 5 33 4 4 3 5 1 1 3 42 5 5 2 1 1 1 2 13 4 5 3 3 3 3 34 2 4 2 1 1 1 1 12 2 2 2 4 2 2 2 22 4 2 3 3 3 3 32 4 3 2 5 1 1 1 13 3 3 3 3 3 3 5 31 1 1 3 3 3 3 3 32 2 3 2 1 1 1 1 12 2 2 2 2 2 2 2 24 2 5 3 5 3 3 3 32 2 4 2 2 2 2 2 11 1 4 1 5 1 1 1 11 1 1 1 1 1 1 1 12 2 2 2 4 1 1 1 1
3 1 3 4 3 2 3 3 31 5 1 5 3 2 1 4 1
3 4 1 1 1 4 3 5 13 4 3 5 3 5 5 5 52 1 1 1 1 1 1 13 3 3 3 3 4 4 3 14 1 1 2 3 2 4 4 23 3 4 2 2 5 3 2 3
3 3 1 1 4 4 3 2 21 1 1 1 1 1 1 1 14 1 1 1 2 1 1 1 13 1 3 3 1 5 1 1 33 2 1 1 2 4 4 2 4
3 2 2 2 2 4 2 2 22 2 2 2 2 2 2 2 22 4 2 2 2 5 2 3 22 2 2 2 2 4 2 2 23 4 1 1 2 2 2 3 42 2 2 4 1 5 2 4 2
2 2 2 4 2 4 23 1 1 1 1 1 3 1 11 1 1 1 5 1 1 1 12 2 2 2 2 4 2 2 23 2 1 5 1 4 5 51 1 1 1 1 1 1 1 13 3 5 1 3 3 4 3 31 1 1 1 1 4 5 1 11 1 1 1 5 4 3 4 12 2 2 2 2 2 2 2 23 3 4 1 2 5 2 4 11 1 3 1 1 4 1 31 1 1 1 4 2 2 2 23 3 3 3 2 2 2 2 21 1 1 1 1 1 1 1 12 2 2 2 2 5 2 2 13 3 3 4 2 3 2 31 1 1 1 2 5 1 1 13 3 3 3 2 3 3 5 23 3 3 3 1 1 1 1 11 1 1 1 1 1 1 1 12 2 2 4 2 2 2 2 23 3 3 3 1 5 1 1 11 1 1 1 1 2 1 2 1
1 1 1 1 5 1 1 11 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1
4 2 4 3 The decision was made that I didn't enjo 5 1 4 1 Was not provided with enough informati
1 3 5 1 I transferred courses as I was unable to 3 5 5 4 There was really no help or motivation fr 1 2 1 1 I just couldnt juggle fulltime work, a fam 3 3 2 The course was not theoretical enough. 4 4 5 3 The main reason is I'm the student spea 3 2 3 2 Pressures of changes in workplace increa
1 3 2 Work and where my life direction was he1 1 4 1 The course content and the other individ 1 2 1 5 I picked the wrong degree, it was nothin 3 3 4 3 Too hard to fit the full time work I had a 2 2 2 5 I left IT because of the lack of decent jo
My reason was quite personal. I decided
2 2 4 4 I suffer from clinical depression and my 2 2 2 4 I picked the wrong degree because I com 2 2 3 2 Work Commitments and Study Timetable2 2 2 3 Mainly I left because I didn't like the teac 1 3 4 4 I wasn't suited to the course. My ideal w 5 2 5 5 Two Reasons. A) Dad died. B) Course w 2 2 4 2 Financial struggle. I was unable to suppo 1 1 4 3 Had a job offer which was to good to pas 1 1 1 1 I was too young, solialising too much an 2 2 4 2 The Industry is very hard now to get into 1 1 5 2 The lack of financial aid which caused gr 1 1 4 5 As an international student I had an IT b 3 3 3 5 No good knowledge background in comp 1 1 4 5 The course content material was paced r 1 3 4 4 the way the course was structured and p 2 2 2 5 Was a mistake as meant to go into Econ 1 3 5 5 I had to work more to pay rent/bills whic 1 3 3 4 Course content wasn't practical nor busin 2 2 2 4 I found the material very dry/boring, and 2 2 2 5 I found Computer Science rather boring 1 1 1 1 Teachers attitude, lack of ability and gen 2 2 2 3 Conflict with work commitments. (I woul 3 3 4 3 Switched to double degree BSc+BEng.1 1 2 1 There simply wasn't enough time to com 1 3 5 1 The teachers did not care about teaching 1 1 1 1 Hubby said tutty bye and I had two youn 1 1 4 5 When I applied for an ICT course I thoug 2 2 2 2 Personal Circumstance - Depression1 1 3 1 I got work in IT. I would still like to go b 1 2 4 2 I withdrew basically because of the cost 1 1 1 5 The main reason I ended up having to w 1 1 1 1 Poor course content. I am not willing to 1 1 1 1 None. I never completed my honors deg
As above, IBL was a real eye opener, e 4 10/20/2011Subject enrolment system was extreme 5 20-30
Providing evening, summer intensive an 6 30-40Murdoch is a business and did not reall 5 20-30I had a great time doing the 1 unit I co 3 05/10/2011There is nowhere near enough credit fo 6 30-40Yes, It's like: " Do you know or unders 3 05/10/2011Previous experiences with Murdoch Uni 6 30-40
The Uni was good. It was my life direct 6 30-40I enrolled and commenced a Masters in 7 40During the tutorials there was no chanc 1 0
t the time. 7 40Needs to be more career focused. 6 30-40
further study wasn't what I wanted to 4 10/20/2011
The lecturing staff, for the most part at 3 05/10/2011I was very bored at the 3 hour lectures 3 05/10/2011
e 7 40 ching style at Murdoch. I found it more 7 40
Some academics are better suited to te 5 20-30Coming in as a Mature aged student i f 5 20-30I enjoyed studying at university, and no 6 30-40
ss on. 4 10/20/2011There were no outside factors bar my o 3 05/10/2011The University was excellent, the cours 7 40I would like to have had more support 4 10/20/2011N/AMy withdrawal was not linked to the na 3 05/10/2011The only thing that i can underline is it 4 10/20/2011
presented to students was poor. in heinz 5 20-30I actually transferred from Chemistry to Economics this year, but accidently transferred to Compute The university is well equipped but the 5 20-30FYI I currently work in the ICT industry 5 20-30
d the people (students/my peers) very u 3 05/10/2011 and challenging, I didn't understand the 3 05/10/2011
Some lectures were either read directly 3 05/10/2011 d have loved to continue with studies a 7 40
The lecturer for one unit kept throwing 6 30-40FIND SOME PEOPLE THAT CARE ABOU 4 10/20/2011I was an external student. Since then b 1 0
ght it was what I wanted to do. A year 4 10/20/2011Yes, and this is more likely my fault, I 5 20-30I would have preferred to use Linux ma 1 0i found it interesting that you asked ab 6 30-40Having moved to Toronto, Canada in 2 5 20-30There was very little support or encour 5 20-30A question along the lines of 'Why did y 7 40
1 2 Computers have been a passion of mine si 2 1
3 0 it was a do I misunderstood the subject being covered 1 11 12 13 12 0 Knowledge I was interested in the topic and was seeki
2 13 1 This series of questions is confusing. I orig 3 1
12 15 1
3 13 12 11 13 0 Graphic DeIt was my 2nd degree attempt. I already h 2 2 i wanted to do journalism but already had 2 13 12 15 0 Journalism Because I wanted a challenge and a chang 2 1
3 12 13 2 it looked and sounded very interesting and
er Science, but they fixed it up the day after and probably shouldn't be in this survey. Just thought I'd let yo 4 11 0 BCM at UWBCM was far too theoretical - not applicabl 3 12 13 11 1
1 11 11 2 In 1966 I passed the commonwealth test a 2 11 1
0 I was the s I like programming, and there's more work 3 11 0 Computer SI was assured as a naive student that InfoT 1 13 1
1 2 1 1 1 11 2 1 1 0 Too busy w 0
0 psychology 2 1 1 1 00 B. eng Com 0 enrollment 1 2 0 because wa 01 2 2 1 0 didnt think 01 2 1 1 0 External st 01 2 1 2 1 10 Bachelor of 2 1 1 0 Live remote 0
1 2 1 1 1 01 2 2 1 0 For my Bac 11 2 1 1 1 21 2 1 1 0 missed it d 01 2 1 1 1 01 2 1 1 1
1 2 1 1 1 11 2 1 1 1 01 2 1 1 2 20 Bachelor B 2 1 1 2 20 Bach of Art 2 1 1 1 01 2 1 1 0 work comm 21 2 1 1 1 11 2 1 2 21 2 1 1 1 02 2 1 1 1 11 2 1 1 0 work comm 0
1 2 1 1 1 11 2 1 1 1 01 2 1 1 1 0
u know. Thanks.1 2 1 1 1 00 Bachelor of 2 1 1 1 01 2 1 1 1 01 2 1 1 1 21 2 1 1 1 11 2 1 1 1 0
1 2 1 1 2 11 2 1 1 1 02 2 2 1 0 I do not re 01 2 1 1 1 20 Bachelor of 2 2 1 1 00 Diploma of 2 1 1 1 12 2 2 1 0 I work at t 21 2 1 1 1 10 Bachelor of 2 2 1 0 I wasn't th 00 BSc Compu 2 2 1 1 1
4 1 English 1 1 2Was not in 2 1 English and 1 2 15 Russian
not relevan 1 4 english 1 3 2was there a 5 4 english 1 1 10wasnt inter 1 5 english 1 1 2External st 2 5 Yes. 1 1 15 Human, fro
5 1 Chinese 2 1 4Live remote 3 5 English 1 2 2
Time and t 4 4 Australian 1 1 21 1 English 1 1 23 1 English 1 3 2
dont remem 2 4 english 1 1 2Didnt feel t 2 4 English 1 1 2
1 English 1 3 2
4 1 English 1 3 2I can't rem 2 1 english 1 3 15 russia
3 2 English 1 3 22 1 English 1 3 2
Wasn't awa 3 6 English and 1 1 22 1 1.01E+23 2 15 Lemuira2 3 English 1 2 23 1 English 1 1 2
Didn't reall 5 1 English 1 2 24 1 English 1 2 2
there were 4 1 english 1 1 11
4 5 Dinka 2 1 15 SudanUsually due 5 1 English 1 3 2there were 4 1 english 1 3 2
they were 5 1 English 1 3 2Not interes 3 1 English 1 2 2None were 3 1 English 1 3 2
3 1 English 1 2 24 4 English 1 3 3
Not able to 3 1 English 1 3 2 Australia
1 1 English 1 1 2Not time. 2 4 english 1 2 2Don't reme 2 5 English 1 1 3
2 1 English 1 1 2Too Old :-) 3 1 English 1 1 10
3 1 English 1 2 22 1 English 1 2 22 1 English 1 3 2
As above. 1 4 English 1 1 22 3 English 1 3 2
192
APPENDIX H: SAMPLE OF INTERVIEWEE PERMISSION FORM
[Interviewee (by phone) information sheet with contact details]
Interviews of Former University ICT Students
Understanding the reasons why ICT students withdraw from their degrees
Thank you for agreeing to participate in this interview. Your participation in this research is voluntary and you are free to withdraw at any time. All your responses will be treated in strictest confidence, and your answers and comments will not be published in any form that identifies you.
This interview consists of a small number of questions designed to give you the opportunity to comment on your experience or to highlight other issues I may have overlooked.
The information you give me will be used to improve the quality of both course design and the student experience for students who take courses like the one you did and I will send you a summary of my findings if you would like this.
My name is Madeleine Roberts, I am a Masters student, and I intend to use the data from this interview – which will not identify you in any way – for my Thesis and for Journal articles. My contact details are: [email protected] or you can call me on: 0418 692 263.
This research has been given approval by the University of Wollongong’s Ethics Committee (Approval Number: HE10/198) and if you have any complaints or reservations about any aspect of your participation in this research you may contact the project manager, Dr Tony Koppi ([email protected]), or the Human Research Ethics Committee, Research Office, University of Wollongong NSW 2522 or phone (02) 4221 4457. Any complaint you make will be treated in confidence and investigated fully, and you will be informed of the outcome.
Are you happy to give me your verbal agreement to continue with the interview?
Verbal agreement given: YES / NO Interview Date:
Interviewee Name:
193
APPENDIX I: ACDICT SURVEY QUESTIONS
Page 1
ACDICT Heads of SchoolsACDICT Heads of SchoolsACDICT Heads of SchoolsACDICT Heads of Schools
This survey of academic leaders in ICT has the aim of identifying key aspects of the ICT curriculum that will be of benefit to all participating institutions. This is an ALTCsupported project and concerns four aspects of the ICT curriculum: 1. Improving poor and erroneous perceptions of the ICT profession 2. Understanding students better, especially with respect to the lack of women and attrition 3. Policy and practices concerned with workintegrated learning 4. Understanding the nexus between teachingresearchindustrylearning in ICT The survey focus is on domestic students and should about 2030 minutes. All responses will be treated confidentially and no information will be released that could identify an individual or organisation unless prior approval is given. Participation is entirely voluntary and consent is given when the survey is submitted. Any complaint concerning the way this research is conducted should be addressed to the University of Wollongong, Human Research Ethics Committee, Research Office, Wollongong NSW 2522.
2. Please rate your response to the following statement:
3. Please rate your response to the following statement:
A National Perspective on the ICT Curriculum
1. Our total domestic undergraduate enrolment numbers in ICT are:
Strongly disagree
Disagree Neutral AgreeStrongly agree
To increase enrolments in ICT, student perceptions of the ICT profession would need to be more positive.
nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Our school/faculty has an effective ICT outreach program. nmlkj nmlkj nmlkj nmlkj nmlkj
Steady
nmlkj Falling
nmlkj Increasing
nmlkj
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ACDICT Heads of SchoolsACDICT Heads of SchoolsACDICT Heads of SchoolsACDICT Heads of Schools
4. Effective outreach activities at my institution that are attracting students to ICT are:
5. Additional activities that we should be doing as an institution to improve perceptions of ICT and thereby attract more students are:
6. A collective activity by universities that would improve ICT perceptions amongst the general public is:
7. Is there anything that universities and industry have done or should be doing together to improve perceptions of ICT amongst the general public?
Outreach Programs Linked to Increased Enrolments in ICT
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8. Our domestic undergraduate enrolment numbers of women in ICT are:
Steady
nmlkj Falling
nmlkj Increasing
nmlkj
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ACDICT Heads of SchoolsACDICT Heads of SchoolsACDICT Heads of SchoolsACDICT Heads of Schools
9. Please rate the following statement:
10. Please rate the following statement:
11. Our strategy for increasing the enrolment of women in ICT is:
12. Additional activities that we should be doing to attract more women into ICT are:
13. Apart from my university's strategies, what would help get more women into ICT is:
14. Please rate the following statements:
15.
16.
Outreach Activities Leading to Greater Enrolments by Women in ICT
Strongly disagree
Disagree Neutral AgreeStrongly agree
We are trying to increase ICT female enrolments. nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Our strategy for increasing female enrolments in ICT is effective. nmlkj nmlkj nmlkj nmlkj nmlkj
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Strongly disagree
Disagree Neutral AgreeStrongly agree
We are unsure of what a genderinclusive ICT curriculum would really look like.
nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
An ICT curriculum that appeals to women would be different to one that appeals to men.
nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
We make an effort to have an ICT curriculum that us explicitly genderinclusive.
nmlkj nmlkj nmlkj nmlkj nmlkj
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ACDICT Heads of SchoolsACDICT Heads of SchoolsACDICT Heads of SchoolsACDICT Heads of Schools17.
18.
Strongly disagree
Disagree Neutral AgreeStrongly agree
There is a link between having a genderinclusive curriculum and the low proportion of women studying ICT.
nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
We would welcome informed guidelines on the practical implementation of a genderinclusive ICT curriculum.
nmlkj nmlkj nmlkj nmlkj nmlkj
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ACDICT Heads of SchoolsACDICT Heads of SchoolsACDICT Heads of SchoolsACDICT Heads of Schools
19. Features of the ICT curriculum that appeal to women are:
20. Features of the ICT curriculum that appeal to men are:
21. Measures we have taken to ensure our ICT curriculum is genderinclusive are:
22. My ICT School or Department provides the following WorkIntegrated Learning (WIL) opportunities for students (mark all relevant ones):
23.
24.
25.
26.
27.
28. Other (please specify):
Features of the ICT Curriculum
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12 month paid industrybased learning (IBL) placement.
gfedc
6 month paid industrybased learning placement.
gfedc
Industrylinked final year project.
gfedc
Unpaid internships.
gfedc
Industryrelevant curricula.
gfedc
Virtual or simulated work experience.
gfedc
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ACDICT Heads of SchoolsACDICT Heads of SchoolsACDICT Heads of SchoolsACDICT Heads of Schools
29. My ICT School or Department:
30.
31.
32.
33.
34.
35.
36.
37.
My ICT School's WIL/IBL Involvement
Strongly disagree
Disagree Neutral AgreeStrongly agree
Provides a high level of resourcing for workintegrated learning (WIL). nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Regards WIL as a key feature of the ICT degree. nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Has general academic staff support for industry engagement through WIL.
nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Actively encourages students to undertake an industrybased learning (IBL) or internship placement.
nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Finds IBL or internship placements for students. nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Actively manages IBL or internship placements. nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Has policies that require industry input into curriculum design. nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Seeks industry input into curriculum design. nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Provides an induction program for students entering IBL or internship placements.
nmlkj nmlkj nmlkj nmlkj nmlkj
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ACDICT Heads of SchoolsACDICT Heads of SchoolsACDICT Heads of SchoolsACDICT Heads of Schools38.
39.
40.
Strongly disagree
Disagree Neutral AgreeStrongly agree
Will not approve an IBL or internship placement that will not provide the student with an appropriate learning experience.
nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Believes that industry should financially support the management of WIL programs.
nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Emphasises the development of generic skills rather than competencies in WIL experiences.
nmlkj nmlkj nmlkj nmlkj nmlkj
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ACDICT Heads of SchoolsACDICT Heads of SchoolsACDICT Heads of SchoolsACDICT Heads of Schools
41. The success of an industrybased learning or internship placement is judged when the student:
42.
43.
44.
45.
46.
47.
48. Other (please specify):
49. Do you have any other comments about WIL:
Judgement of Placement Success
Strongly disagree
Disagree Neutral AgreeStrongly agree
Has completed work tasks as required. nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Is now employable. nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Has gained new technical skills and competencies. nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Did not disrupt normal company operations. nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Has improved understanding of professional responsibility. nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Added value to the company's profitability. nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Gained a variety of work perspectives. nmlkj nmlkj nmlkj nmlkj nmlkj
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ACDICT Heads of SchoolsACDICT Heads of SchoolsACDICT Heads of SchoolsACDICT Heads of Schools
50. Please rate the following statements:
51.
52.
53.
54.
55.
56.
57.
58.
The TeachingResearchIndustryLearning Nexus: Concept and Implications
Strongly disagree
Disagree Neutral AgreeStrongly agree
There is a synergy between teaching, research, industry and learning. nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Industry connections formed by academic staff help student learning. nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Learning by our students is helped by their connections with industry. nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Students would learn better if they had more connection with industry.
nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
The school should have more connection with industry. nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
The research in this school would be better if we had more connection with industry.
nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Disciplinebased research in the school leads to better student learning.
nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
The lack of academic staff involved in disciplinebased research in the school has had a negative impact on student learning.
nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Academic staff who are focused on disciplinebased research are less inclined to be interested in learning and teaching.
nmlkj nmlkj nmlkj nmlkj nmlkj
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ACDICT Heads of SchoolsACDICT Heads of SchoolsACDICT Heads of SchoolsACDICT Heads of Schools59.
60.
61.
62.
63.
64.
65.
66.
67. What more should universities be doing to take advantage of synergies between industry, research and teaching and learning?
Strongly disagree
Disagree Neutral AgreeStrongly agree
The emphasis on research by academic staff involved in disciplinebased research may have a negative impact on student learning.
nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Involving students in research increases their understanding of subjects.
nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Involving students in research stimulates their interest and enthusiasm.
nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Involving students in research improves their research skills. nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Involving students with industry increases their awareness of the problems and issues faced in the industry.
nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Involving students with industry increases their understanding of subjects.
nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Involving students with industry stimulates their interest and enthusiasm.
nmlkj nmlkj nmlkj nmlkj nmlkj
Strongly disagree
Disagree Neutral AgreeStrongly agree
Universities should be doing more to take advantage of synergies between industry, research and teaching and learning.
nmlkj nmlkj nmlkj nmlkj nmlkj
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ACDICT Heads of SchoolsACDICT Heads of SchoolsACDICT Heads of SchoolsACDICT Heads of Schools
68. The name of my ICT Oganisational Unit is:
69. Any other comments:
Survey End
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APPENDIX J: ICT INDUSTRY SURVEY QUESTIONS
1
Survey on ICT industry and university relationships and preparing students for the workplace
The aim of this survey of ICT industry professionals is to inform universities of optimal ways of attracting students and preparing ICT graduates for the workplace. The survey seeks information on:
• work integrated learning • industry contribution to the curriculum • gender issues in ICT • improving perceptions of ICT
The survey consists mainly of tick boxes and also free text responses. It should take no more than 15–20 min to complete. When completed it should be mailed back via the stamped addressed envelope included. Survey results may be obtained from Tony Koppi, [email protected] Support is provided by the Australian Learning and Teaching Council (Project PP9-1274) on behalf of all Australian universities and managed by the universities of Murdoch, Queensland, Swinburne and Wollongong (lead institution). Assistance and advice of the Australian Council of Deans of ICT and the Australian Computer Society are also gratefully acknowledged.
Ethics Statement All responses will be treated confidentially and no information will be released that could identify an individual or organization unless prior approval is given. Aggregate findings will be made available in any reports. Participation is entirely voluntary and consent to participate is indicated by completing and returning the questionnaire. The survey is anonymous and no individual will be identified. Any complaint concerning the way this research is conducted should be addressed to the University of Wollongong, Human Research Ethics Committee, Research Office, Wollongong, NSW 2522.
Abbreviations used In statements below, abbreviations used are: SD = Strongly Disagree; D = Disagree; N = Neutral; A = Agree; SA = Strongly Agree – please pick one for each statement.
2
University and industry relationships
SD D N A SA 1. Connections between universities and industry are very valuable
2. Universities should have industry representation on committees that review and/or design the curriculum
3. Universities should seek indirect input to the curriculum through industry bodies (e.g. AIIA) or from government agencies/reports
Industry should be involved in curriculum by: SD D N A SA 4. advising university curriculum developers
5. working directly with university curriculum developers
6. endorsing/approving curriculum decisions
7. other please specify:
Industry should be involved in curriculum design at: SD D N A SA 8. degree formulization stage (i.e. focus, themes, etc)
9. program accreditation stage (formal approval/endorsement)
10. program implementation stage ( assisting with delivery etc)
11. times when degree programs have a major change
12. annual review stage
13. every 5 years as part of a review or reaccreditation
Industry should provide advice relating to: SD D N A SA 14. high level themes or focus of courses
15. the structures of degrees
16. the syllabus of individual subjects/units (including topics and skills)
17. the teaching methods used
3
Please respond to 18–20 only if you have previous involvement in a university curriculum (e.g. serving on an advisory committee); otherwise go to 21. SD D N A SA 18. Universities welcome advice regarding curriculum
19. Universities act on the advice regarding curriculum provided by industry
20. Universities provide feedback on how they have responded to advice
Authentic Work Integrated Learning (WIL) is: SD D N A SA 21. students working in an industry placement for 3 to 12 months
22. students working on a project that has been set by industry during their studies
23. a simulated workplace environment within a university
24. curriculum that has direct industry links and is taught with industry guest speakers
25. other please specify
I believe that the following are extremely important experiences when recruiting graduates: SD D N A SA 26. that the student has worked in an industry placement for 3 to 12
months
27. that the student has worked on a project that has been set by industry during their studies
28. that the student has experienced a simulated workplace environment within a university
29. that the student has been exposed to curriculum that has direct industry links including industry guest speakers
30. other please specify:
4
The major benefit for employer involvement in WIL programs is: SD D N A SA 31. graduate recruitment
32. service to the community
33. service to the ICT industry
34. cost-effective labour for projects
35. other please specify:
My organization encourages engagement with universities such as: SD D N A SA 36. participation on university committees
37. provision of case studies or project ideas
38. hosting site visits
39. provision of guest speakers
40. hosting internship or work placement students
41. research linkages
42. other please specify:
The attributes or skills that WIL should develop in students are: SD D N A SA 43. technical skills (e.g. a new programming language)
44. interpersonal skills (e.g. communication, teamwork, etc)
45. workplace skills (e.g. professionalism, realistic expectations, etc)
46. project management, planning and problem solving
47. ensuring the student is immediately productive when employed
48. other please specify:
5
If NOT involved with students working with your organization, please go to 58 (next page)
49. Appropriate length of placement (please rank 1, 2, and 3):
3 months 6 months 12 months
50. Induction of the students in my organization involves (tick all that apply):
a structured induction program a mentor a form nil other please specify:
51. Level of involvement a university should have during a placement (please rank 1, 2, 3,): monthly visits two visits one visit 52. Other kind of visit, please specify:
SD D N A SA 53. A student on placement is regarded as an employee of the
organization
54. My organization would take on a student who only has a ‘pass’ average in academic studies for a WIL placement
55. My organization would take on a international student for a WIL placement
56. English language skills are critical for a successful WIL placement
57. Industry should pay students during placements
58. The government should support/subsidize payments for student in placements
6
Further relationships SD D N A SA 59. Research relationships with universities or university staff are
very useful to my organization
60. My organization would like to have more research relationships with university staff
61. Consultancies by university ICT academics would be very useful to my organization
62. Short courses provided by universities would be very useful to my organization
63. What more could universities do to strengthen connections with industry? Please specify:
64. What more could my organization or industry do to foster connections with universities? Please
specify:
Gender issues SD D N A SA 65. ICT is generally male dominated with a masculine culture
66. My workplace is male dominated with a masculine culture
67. Female perspectives and approaches to ICT issues and challenges are valuable
68. In my workplace, female perspectives and approaches to ICT issues and challenges are actively sought
69. Regarding gender issues, any suggestions for universities in preparing students for the workplace? Please specify:
7
Perception issues SD D N A SA 70. ICT has a poor perception amongst the general public
71. The ICT industry in general tries to improve those perceptions
72. Industry attempts to improve perceptions are successful
73. A noteworthy example of industry attempts to improve perceptions is:
SD D N A SA 74. My company makes efforts to improve ICT perceptions amongst
the general public
75. These efforts by my company are successful
76. An example of a successful effort by my company is:
77. Is there something that industry could do collectively that would improve ICT perceptions
amongst the general public? Please specify:
SD D N A SA 78. Marketing ICT as a profession will improve the perceptions of the
ICT industry amongst the general public
79. Perceptions of ICT would be improved if there were a clear distinction between ICT occupations or careers in the ICT industry
80. These distinctions could be made along the following lines:
8
SD D N A SA 81. ICT perceptions would be improved if professionalism in ICT was
identified and distributed across existing professions such as engineering, business or the design industry
82. Professional ICT accreditation by a professional body improves the perceptions of ICT
General 83. Job title________________________________________________________ 84. Gender
Female Male
85. Type of ICT Industry______________________________________________ 86. Your state
NSW Victoria South Australia Western Australia Northern Territory Queensland ACT Tasmania
87. If you are willing to be contacted further about these and other issues concerning ICT, please
provide contact details.
Thank you for completing this survey
195
APPENDIX K: HUMAN RESEARCH ETHICS COMMITTEE PERMISSION LETTERS
196
APPENDIX L: TABLES OF QUANTITATIVE DATA
197
Final Ranking Female N = 26
Female %
Male N = 106
Male % Total N = 132
Total %
Top 10% 9 34.6 22 20.7 31 23.5
Next 20% 12 46.2 45 42.4 57 43.2
Middle 40% 5 19.2 34 32.1 39 29.5
Lower 20% 0 0 2 1.9 2 1.5
Bottom 10% 0 0 3 2.8 3 2.3
Table L1: Final ranking at high school of survey respondents
Ranking Before Quitting
Female N = 26
Female %
Male N = 109
Male % Total N = 135
Total %
Top 10% 3 11.5% 9 8.3% 12 8.9
Next 20% 4 15.4% 33 30.3% 37 27.4
Middle 40% 12 46.2% 35 32% 47 34.8
Lower 20% 3 11.5% 21 19.3 24 17.8
Bottom 10% 4 15.4% 11 10.1 15 11.1
Table L2: Final ranking in the ICT degree before students quit
Gross Income
Female N = 25
Female %
Male N = 111
Male % Total N = 136
Total %
$80,000+ 6 24% 49 44% 55 40%
$40,000 – $80,000
11 44% 31 28% 42 31%
$0 – $40,000
8 32% 31 28% 39 29%
Table L3: Parent’s/Guardian’s Combined Gross Income while respondents were students
Hours
PW
Female N = 25
Female %
Male N = 111
Male % Total N = 136
Total %
0 4 16% 11 10% 15 11%
1 – 5 2 8% 8 7% 10 7%
6 – 10 3 12% 19 17% 22 16%
11 – 20 7 28% 26 23% 33 24%
21 – 30 4 16% 16 14% 20 14%
31 – 40 3 12% 17 15% 20 14%
40+ 2 8% 14 12% 16 12%
Table L4: Hours worked per week by survey respondents while they were students
198
Marital Status Female N = 28
Female %
Male N = 108
Male %
Total N = 136
Total %
Single 18 64% 80 74% 98 72%
Single with dependent child(ren)
1 4% 1 1% 2 1%
Partner & no child(ren)
4 14% 16 15% 20 15%
Partner & child(ren) 5 18% 11 10% 16 12%
Table L5: Marital status of respondents when students first enrolled
Ethnicity/Origin Female N = 26
Female %
Male N = 110
Male % Total N = 136
Total %
Arab 0 0% 1 1% 1 0.7
Australian 22 84.6% 78 71% 100 73.5
British 1 3.8% 3 2.7% 4 2.9
Chinese 0 0% 7 6% 7 5%
Croatian 0 0% 0 0% 0 0%
Greek 0 0% 0 0% 0 0%
Indian 1 3.8% 0 0% 1 0.7%
Indonesian 0 0% 2 1.8% 2 1.6%
Italian 0 0% 0 0% 0 0%
Malaysian 0 0% 2 1.8% 2 1.6%
Singaporean 1 3.8% 1 1% 2 1.6%
Spanish 0 0% 0 0% 0 0%
Thai 0 0% 1 1% 1 0.7%
Vietnamese 0 0% 1 1% 1 0.7%
Other 1 3.8% 14 12.7% 15 11%
Table L6: Ethnicity or country of origin
199
Language Female N = 29
Female %
Male N = 107
Male % Total N = 136
Total %
Arabic 0 0% 1 0.9% 1 0.7%
Cantonese 0 0% 3 3% 3 2.2%
English 28 96.5 92 86% 120 88.3%
Hindi 0 0% 1 0.9% 1 0.7%%
Indonesian 0 0% 1 0.9% 1 0.7%%
Korean 0 0% 1 0.9% 1 0.7%%
Mandarin 0 0% 2 1.9% 2 1.5%
Persian 0 0% 1 0.9% 1 0.7%
Polish 1 3.5 0 0% 1 0.7%
Russian 0 0% 1 0.9% 1 0.7%
Sinhala 0 0% 1 0.9% 1 0.7%
Tagalog 0 0% 1 0.9% 1 0.7%
Thai 0 0% 1 0.9% 1 0.7%
Vietnamese 0 0% 1 0.9% 1 0.7%
Table L7: Language spoken at home
Enrolment Status
Female N = 29
Female %
Male N = 124
Male % Total N = 153
Total %
Full-time 20 69% 93 75% 113 74%
Part-time 9 31% 31 25% 40 26%
Enrolment Status
Female N = 26
Female %
Male N = 110
Male % Total N = 136
Total %
Domestic 26 100% 100 90.9% 126 92.6%
International 0 0% 10 9.1% 10 7.4%
Enrolment Status
Female N = 26
Female %
Male N = 108
Male % Total N = 134
Total %
Undergraduate 24 92.3% 92 85.2% 116 86.6%
Postgraduate 2 7.7% 16 14.8% 18 13.4%
Table L8: Enrolment status of students
200
Enrolled Discipline
Female N = 28
Female %
Male N = 107
Male % Total N = 135
Total %
Computer Science 5 17.9% 41 38.3% 46 34.1%
Computer Systems Engineering
0 0% 2 1.9% 2 1.5%
Electrical Engineering 0 0% 4 3.7% 4 3.0%
Information Technology
18 64.3% 40 37.4% 58 42.9%
Information Systems 4 14.3% 14 13.1% 18 13.3%
Software Engineering 1 3.5% 3 2.8% 4 3.0%
Telecommunications Engineering
0 0% 3 2.8% 3 2.2%
Table L9: Enrolled discipline of students
Timing of Quitting Female N = 21
Female %
Male N = 100
Male %
Total N = 121
Total %
First half 16 76.2% 52 52.0% 68 56.2%
Second half 5 23.8% 48 48.0% 53 43.8%
Table L10: Timing of quitting in the first year
ICT Degree First Choice
Female
N = 26
Female
%
Male N
= 94
Male % Total N
= 120
Total
% Yes 16 61.5 88 93.6 104 86.7
No 10 38.5 8 8.5 16 13.3
Table L11: Preference for ICT degree
Reason For Not Attending Functions No. None organised 13
Not aware that any had been organised 11
Not interested 17
Work and/or family commitments 8
Too shy 4
Table L12: Reasons given by students for not attending school or faculty functions
Main Reason for Quitting
Female N = 29
Female %
Male N = 125
Male % Total N = 154
Total %
Personal 3 10.3% 40 32% 43 27.9%
Course 1 3.5% 27 21.6% 28 18.9%
Both 25 86.2% 58 46.4% 82 53.2%
Table L13: Main reason for quitting chosen by students
201
University Experience
Reason
Total
Num.
SD
%
D
%
N
%
A
%
SA
% There were too many distractions preventing me from
concentrating on my studies
153 13.7 26.1 19.6 32.7 17.8
Organising a suitable timetable, with no clashes, was
challenging
152 20.4 34.9 17.1 18.4 9.2
I couldn't get help when I needed it 150 19.3 37.3 16.7 18.0 8.7
The University staff were not friendly 151 25.2 35.8 21.9 11.9 5.3
The University facilities were not adequate 152 23.7 43.4 18.4 10.5 3.9
There were no opportunities to socialise 151 19.9 39.7 27.2 9.3 4.0
Attending evening classes posed a security risk 152 38.8 38.2 19.1 3.3 0.7
Table L14: Responses to reasons for attrition associated with the university environment
(SD = Strongly Disagree to SA = Strongly Agree)
Course Experiences
Reason
Total
Num.
SD
%
D
%
N
%
A
%
SA
%
Teaching The classes were boring 151 9.9 27.8 19.9 25.8 16.6
The pace of teaching was too fast 152 17.1 33.6 17.1 20.4 11.8
The teachers didn't explain the exercises 151 13.9 38.4 19.2 19.9 8.6
I wasn't encouraged to do well by the teachers 151 15.2 34.4 27.8 16.6 6.0
The teaching methods were harsh and confrontational 152 20.4 43.4 24.3 10.5 1.3
The teachers were not prepared 152 18.4 51.3 20.4 5.9 3.9
The teachers' knowledge was out of date 152 15.1 44.1 25.0 11.8 3.9
Table L15: Reasons for attrition associated with the course: Teaching
(SD = Strongly Disagree to SA = Strongly Agree)
202
Course Experiences
Reason
Total
Num.
SD
%
D
%
N
%
A
%
SA
%
Course The course didn't have a workplace focus 151 9.9 27.2 25.8 25.8 11.3
The course lacked practical applications 151 12.6 39.1 17.2 19.9 11.3
The course didn't have a business focus 152 14.5 26.3 28.3 21.7 9.2
The course was too theoretical 152 13.8 34.9 22.4 22.4 6.6
The course was poorly structured 149 12.8 34.9 25.5 15.4 11.4
There were too many assignments 147 13.6 36.7 27.9 18.4 3.4
The course was too mathematical 151 15.9 46.4 19.2 12.6 6.0
Table L16: Reasons for attrition associated with the course: Course
(SD = Strongly Disagree to SA = Strongly Agree)
Course Experiences Reason
Total Num.
SD %
D %
N %
A %
SA %
Teaching and Learning Environment I didn't feel I fitted in or belonged 147 18.4 27.9 17.7 24.5 11.6 Academic environment did not suit my learning style 152 13.2 32.9 18.4 23.7 11.8 The teaching environment was not welcoming 152 15.1 38.2 21.1 17.1 8.6 I was in the minority in my classes 146 24.7 32.2 17.8 16.4 8.9 The focus was on individual activities rather than groups
149 18.1 32.2 30.2 13.4 6.0
The course was too competitive 151 17.9 40.4 28.5 11.9 1.3 Table L17: Reasons for attrition associated with the course: Teaching and Learning
(SD = Strongly Disagree to SA = Strongly Agree)
Course Experiences
Reason
Total
Num.
SD
%
D
%
N
%
A
%
SA
%
Preparedness and other student issues The course didn't meet my expectations 148 8.8 20.9 16.9 30.4 23.0
I didn't enjoy attending classes 146 12.3 18.5 19.9 32.9 16.4
I didn't understand the concepts 152 17.1 33.6 15.8 24.3 9.2
My results were not as high as I expected 149 12.1 29.5 28.9 22.1 7.4
I didn’t make friends with classmates 145 15.2 33.8 24.1 17.9 9.0
I didn’t understand the meaning of the terms used
in the course
149 18.8 40.3 18.1 20.1 2.7
I didn't have the expected background knowledge 148 23.6 35.1 16.2 15.5 9.5
I felt it was unacceptable to be smart 147 36.7 44.2 14.3 4.8 0.0
Table L18: Reasons for attrition associated with the course: Preparedness and Other Student Issues
(SD = Strongly Disagree to SA = Strongly Agree)
203
Life Experience
Reason
Total
Num.
SD
%
D
%
N
%
A
%
SA
% I picked the wrong degree 144 18.1 17.4 20.8 20.8 22.9
Attending university was too expensive 142 23.9 21.8 16.9 21.1 16.6
There was conflict with my work commitments 143 26.6 23.1 14.0 21.0 15.4
My timetable didn't fit with my work commitments 144 22.2 25.0 19.4 17.4 16.0
Travelling to University was/is difficult because of
distance
145 31.7 25.5 17.9 15.2 9.7
Travelling to University was/is difficult because of
transport
145 30.3 29.0 17.2 14.5 9.0
I couldn't get financial aid 141 32.6 25.5 22.0 9.2 10.6
My timetable didn't fit with the transport timetable 143 30.8 29.4 25.2 9.8 4.9
My family didn't help me to study at home 144 37.5 25.7 23.6 9.7 3.5
Living at home was too difficult 145 33.1 31.0 26.9 6.2 2.8
I became very ill or was involved in a serious
accident
145 47.6 24.8 19.3 4.8 3.4
Studying at University wasn't as important as
socialising with my friends
144 34.0 37.5 20.8 6.3 1.4
Living away from home was too difficult 144 31.9 26.4 33.3 2.8 5.6
A family member died or was very ill or had a
serious accident
144 50.7 25.0 17.4 4.2 2.8
I lost my job 143 48.3 25.2 21.0 3.5 2.1
I missed my family 144 34.7 32.6 27.8 3.5 1.4
I or my partner got pregnant. 144 52.1 20.1 23.6 2.8 1.4
Living in student accommodation was too difficult 142 26.8 21.1 47.9 3.5 0.7
Table L19: Reasons associated with students’ and their lives
(SD = Strongly Disagree to SA = Strongly Agree)
204
Reason Survey Section A + SA S, R or S&R
The course didn't meet my expectations Preparedness & other student issues
53.4% R
I didn't enjoy attending classes Preparedness & other student issues
51.3% S & R
There were too many distractions preventing me from concentrating on my studies
University Experiences 50.5% S
I picked the wrong degree Life Experiences 43.7% R The classes were boring Teaching Experiences 42.4% S & R Attending university was too expensive Life Experiences 37.7% R The course didn't have a workplace focus Course Experiences 37.1% R There was conflict with my work commitments Life Experiences 36.4% R I didn't feel I fitted in or belonged Teaching & Learning
Environment 36.1% S
Academic environment did not suit my learning style
Teaching & Learning Environment
35.5% R
I didn't understand the concepts Life Experiences 33.5% R My timetable didn't fit with my work commitments
Life Experiences 33.4% R
Table L20: Significant contributory factors leading to attrition
Female Experience
Reason
Total
Num.
SD
%
D
%
N
%
A
%
SA
% There were no or few females in the classes 27 11.1 3.7 22.2 48.1 14.8
The course content was male oriented 29 20.7 24.1 27.6 20.7 6.9
Male staff didn't encourage me to participate 27 25.9 33.3 22.2 18.5 0.0
Students acted or spoke in a sexist manner 28 28.6 32.1 32.1 3.6 3.6
Male students wouldn't let me participate 27 25.9 40.7 29.6 3.7 0.0
Male staff acted or spoke in a sexist manner 27 33.3 37.0 25.9 0.0 3.7
Table L21: Female responses to gender specific reasons for leaving an ICT course
(SD = Strongly Disagree to SA = Strongly Agree)
Male Experience
Reason
Total
Num.
SD
%
D
%
N
%
A
%
SA
% There were no or few females in the classes 118 9.3 20.3 23.7 28.0 18.6
The course content was male oriented 123 22.8 35.8 32.5 4.9 4.1
Male staff didn't encourage me to participate 120 30.0 40.0 24.2 4.2 1.7
Students acted or spoke in a sexist manner 120 37.5 41.7 16.7 2.5 1.7
Male students wouldn't let me participate 119 37.0 41.2 20.2 1.7 0.0
Male staff acted or spoke in a sexist manner 121 43.0 37.2 17.4 2.5 0.0
Table L22: Male responses to gender specific reasons for leaving an ICT course
(SD = Strongly Disagree to SA = Strongly Agree)
205
Female and Male Experience Reason
Females Mean Std. dev.
Males Mean Std. dev.
Sign.
There were too many distractions preventing me from concentrating on my studies (S)
2.48 1.06 3.06 1.22 0.021
I didn't understand the concepts 3.54 1.17 2.57 1.21 <0.001 I didn't have the expected background knowledge
3.00 1.30 2.41 1.24 0.026
I didn’t understand the meaning of the terms used in the course
2.96 1.29 2.36 1.02 0.027
My results were not as high as I expected 3.29 1.15 2.73 1.10 0.018 I was in the minority in my classes (S) 3.04 1.26 2.41 1.25 0.021 I picked the wrong degree 3.63 1.36 3.02 1.41 0.043 Table L23: Reasons for attrition with significantly different levels of agreement between females and males
(bold indicates the higher mean)
206
Full-Time and Part-Time Experience Reason
Full-time Mean Std. dev.
Part-time Mean Std. dev.
Sign.
University environment Academic environment did not suit my learning style 3.07 1.18 2.35 1.31 0.002 I couldn't get help when I needed it 2.75 1.23 2.15 1.14 0.009 The university staff were not friendly (S) 2.51 1.17 1.92 0.91 0.002 Course/teaching The course didn't meet my expectations 3.53 1.25 2.98 1.31 0.020 I didn't enjoy attending classes (S) 3.47 1.20 2.58 1.24 <0.001 The classes were boring 3.31 1.22 2.54 1.23 0.001 I didn't feel I fitted in or belonged (S) 3.00 1.31 2.38 1.19 0.009 The pace of teaching was too fast 2.96 1.28 2.21 1.15 0.001 The course was poorly structured 2.94 1.18 2.33 1.14 0.005 I didn't understand the concepts 2.94 1.25 2.23 1.12 0.002 The course was too theoretical 2.93 1.10 2.17 1.11 <0.001 The teachers didn't explain the exercises 2.85 1.20 2.31 1.06 0.014 I didn’t make friends with classmates (S) 2.84 1.24 2.38 0.96 0.022 There were too many assignments 2.76 1.07 2.21 0.86 0.004 The focus was on individual activities rather than groups (S)
2.68 1.15 2.28 0.96 0.050
I didn't have the expected background knowledge 2.66 1.30 2.15 1.12 0.030 The course was too mathematical 2.65 1.12 1.95 0.78 <0.001 I didn’t understand the meaning of the terms used in the course
2.62 1.10 2.08 0.97 0.004
The course was too competitive 2.50 0.99 2.05 0.78 0.004 The teaching methods were harsh and confrontational
2.38 0.99 2.03 0.78 0.025
Life I picked the wrong degree 3.41 1.40 2.38 1.18 <0.001 Travelling to University was/is difficult because of transport
2.55 1.37 2.10 1.05 0.040
My timetable didn't fit with the transport timetable 2.42 1.20 1.92 0.93 0.020 Studying at University wasn't as important as socialising with my friends (S)
2.15 0.99 1.72 0.82 0.016
My timetable didn’t fit with my work commitments 2.61 1.28 3.31 1.56 0.007 There was conflict with my work commitments 2.56 1.30 3.28 1.67 0.007 Table L24: Reasons for attrition with significantly different levels of agreement between participants who were
full-time and those who were part-time (bold indicates the higher mean)
207
Age Difference Experience Reason
20 or under Mean Std. dev.
21 or over Mean Std. dev.
Sign.
Course/teaching I didn't enjoy attending classes (S) 3.83 1.05 2.64 1.20 <0.000 I picked the wrong degree 3.77 1.30 2.48 1.24 <0.000 The course didn't meet my expectations 3.65 1.23 3.11 1.29 0.010 The classes were boring 3.58 1.16 2.62 1.19 <0.001 I didn't understand the concepts 3.15 1.28 2.32 1.09 <0.001 I didn't feel I fitted in or belonged (S) 3.10 1.34 2.57 1.22 0.014 Academic environment did not suit my learning style 3.08 1.16 2.68 1.32 0.048 The course didn’t have a business focus 3.05 1.27 2.64 1.07 0.031 My results were not as high as I expected 3.04 1.13 2.62 1.09 0.023 The pace of teaching was too fast 3.00 1.13 2.51 1.22 0.018 The course was too theoretical 2.96 1.33 2.49 1.13 0.010 The teachers didn't explain the exercises 2.95 1.19 2.44 1.14 0.009 There were too many assignments 2.82 1.08 2.41 0.96 0.015 I didn't have the expected background knowledge 2.78 1.37 2.26 1.11 0.011 I didn’t understand the meaning of the terms used in the course
2.71 1.16 2.24 0.98 0.009
The course was too mathematical 2.66 1.12 2.26 1.02 0.022 The course was too competitive 2.54 0.98 2.22 0.92 0.021
Life
Attending university was too expensive 2.58 1.43 3.10 1.37 0.030 There was conflict with my work commitments 2.44 1.30 3.09 1.51 0.007 A family member died or was very ill or had a serious accident
1.62 0.95 2.06 1.08 0.010
I or my partner got pregnant 1.58 0.84 2.04 1.07 0.005 I lost my job 1.69 0.93 2.03 1.06 0.047
Table L25: Reasons for attrition with significantly different levels of agreement between participants who were
20 or younger when they enrolled and those who were 21 or older (bold indicates the higher mean)
208
Reason Code
Main Reason for Quitting No.
MRQ 1 Course/degree content (R) 25 MRQ 2 Work commitments/conflict (R) 22 MRQ 3 Lecturers/tutors failings (R) 14 MRQ 4 Course structure/design/inherent assumptions (R) 12 MRQ 5 Personal illness/tragedy (S) 10 MRQ 6 Help unavailable/lacking (S & R) 8 MRQ 7 Financial difficulties (R) 6 MRQ 8 Lost passion for ICT (R) 5 MRQ 9 Recognised Prior Learning not given (R) 4 MRQ 10 Pregnancy/marriage (S) 3 MRQ 11 Lack of alternative courses i.e. online versions (R) 3 MRQ 12 Restructuring of course (R) 2 MRQ 13 Permanent residency not granted (R) 2 MRQ 14 Personal immaturity S & R) 2 MRQ 15 Negative work placement experience (R) 1 Total 119 Table L26: Common themes in written responses to open-ended questions about Main Reason for Quitting
(MRQ)
209
Survey Statement Trad %
Mat %
F % M % FT %
PT %
Dom %
Int %
No or few females in class (S)
75.4 <33.3 63 46.6 58.5 <33.3 54 <33.3
Didn’t enjoy classes (S) 70.8 <33.3 59.3 47.1 56.6 <33.3 50 33.3 Picked wrong degree 66.7 <33.3 63 39.3 <33.3 <33.3 44.9 <33.3
Course expectations unmet
64.2 44.4 64.3 50.8 59.3 35 57.5 33.3
Boring classes 56.3 <33.3 51.7 40.2 <33.3 48.2 43.7 33.3 Didn’t fit in or belong (S) 48.5 <33.3 48.1 33.3 <33.3 41.1 37 33.3 Didn’t understand concepts
47.9 <33.3 51.7 <33.3 40.2 <33.3 35.2 33.3
Course lacked workplace focus
45.7 <33.3 42.9 35.8 <33.3 40.5 39.1 33.3
Teaching pace too fast 40.3 <33.3 41.4 <33.3 <33.3 37.2 <33.3 44.4 Teachers didn’t explain exercises
40.3 <33.3 34.5 <33.3 <33.3 <33.3 <33.3 <33.3
Results not as expected 39.7 <33.3 50 <33.3 <33.3 <33.3 <33.3 <33.3
Academic environment unsuitable
38 33.3 37.9 35 <33.3 39.3 37 <33.3
Course lacked practical applications
38 <33.3 39.3 <33.3 <33.3 <33.3 <33.3 <33.3
Course lacked business focus
38 <33.3 35.7 <33.3 <33.3 <33.3 33.6 <33.3
Too many distractions (S)
37.5 43.2 <33.3 44.4 40.7 40 42.2 44.4
I lacked expected knowledge
35.8 <33.3 42.9 <33.3 <33.3 <33.3 <33.3 <33.3
Didn’t make friends (S) 34.8 <33.3 38.5 <33.3 <33.3 <33.3 <33.3 <33.3
Course too theoretical 33.8 <33.3 <33.3 <33.3 34.8 <33.3 <33.3 55.6 Uni too expensive <33.3 41.6 <33.3 40 <33.3 <33.3 35.2 50 In minority in class (S) <33.3 <33.3 51.9 <33.3 <33.3 <33.3 <33.3 <33.3
Couldn’t get help (S) <33.3 <33.3 34.5 <33.3 30.6 <33.3 <33.3 37.5
Didn’t understand terms <33.3 <33.3 42.9 <33.3 <33.3 <33.3 <33.3 <33.3 Conflict with work <33.3 42.9 <33.3 39.3 <33.3 59 39.7 <33.3 Timetable clash with work
<33.3 37.5 <33.3 36.4 <33.3 51.3 36.8 <33.3
Not encouraged by teachers
<33.3 <33.3 <33.3 <33.3 <33.3 <33.3 <33.3 33.3
Course too mathematical <33.3 <33.3 35.7 <33.3 33.3 <33.3 <33.3 <33.3
Table L27: Significant levels of agreement across significant groups of students
210
Sub-Groups Trad %
Mat %
F % M % FT %
PT %
Dom %
Int %
No or few females in class 75.4 <33.3 63 46.6 58.5 <33.3 54 <33.3 Teaching pace too fast 40.3 <33.3 41.4 <33.3 <33.3 37.2 <33.3 44.4 Teachers didn’t explain exercises
40.3 <33.3 34.5 <33.3 <33.3 <33.3 <33.3 <33.3
Results not as expected 39.7 <33.3 50 <33.3 <33.3 <33.3 <33.3 <33.3 Course lacked practical applications
38 <33.3 39.3 <33.3 <33.3 <33.3 <33.3 <33.3
Course lacked business focus
38 <33.3 35.7 <33.3 <33.3 <33.3 33.6 <33.3
I lacked expected knowledge
35.8 <33.3 42.9 <33.3 <33.3 <33.3 <33.3 <33.3
Didn’t make friends 34.8 <33.3 38.5 <33.3 <33.3 <33.3 <33.3 <33.3 Course too theoretical 33.8 <33.3 <33.3 <33.3 34.8 <33.3 <33.3 55.6 In minority in class <33.3 <33.3 51.9 <33.3 <33.3 <33.3 <33.3 <33.3 Couldn’t get help <33.3 <33.3 34.5 <33.3 30.6 <33.3 <33.3 37.5
Didn’t understand terms <33.3 <33.3 42.9 <33.3 <33.3 <33.3 <33.3 <33.3
Not encouraged by teachers
<33.3 <33.3 <33.3 <33.3 <33.3 <33.3 <33.3 33.3
Course too mathematical <33.3 <33.3 35.7 <33.3 33.3 <33.3 <33.3 <33.3
Table L28: Contributory factors for sub-groups of students
Number of Domestic Student Enrolments No. % Steady 19 46.3
Falling 12 29.3
Increasing 10 24.4
Table L29: Frequency of responses to question on status of domestic undergraduate student enrolment numbers
Number of Female Undergraduate Enrolments No. % Steady 22 53.7
Falling 15 36.6
Increasing 4 9.7
Table L30: Frequency of responses to question on status of undergraduate domestic female enrolment numbers
SD D N A SA 3. We are trying to increase ICT female enrolments
1 2.2%
6 13%
4 8,7%
20 43.5%
15 32.6%
4. Our strategy for increasing female enrolments in ICT is effective
1 2.3%
22 50%
13 29.5%
6 13.6%
2 4.5%
Table L31: Frequency of rankings for statements on increasing female enrolments
(SD = strongly disagree, SA = strongly agree)
211
SD D N A SA 5. We are unsure of what a gender-inclusive ICT
curriculum would really look like 2 4.4%
9 20%
6 13.3%
21 46.7%
7 15.6%
6. An ICT curriculum that appeals to women would be different to one that appeals to men
6 13.3%
15 33.3%
13 28.9%
10 22.2%
1 2.2%
7. We make an effort to have an ICT curriculum that is explicitly gender-inclusive
1 2.3%
20 46.5%
10 23.3%
8 18.6%
4 9.3%
8. There is a link between having a gender-inclusive curriculum and the low proportion of women studying ICT
3 7.3%
14 34.1%
14 34.1%
10 24.4%
0 0%
9. We would welcome informed guidelines on the practical implementation of a gender-inclusive ICT curriculum
1 2.2%
0 0%
4 8.9%
25 55.6%
15 33.3%
Table L32: Frequency of rankings for statements on a gender-inclusive curriculum
(SD = strongly disagree, SA = strongly agree)
Additional Strategies to Increase Female Enrolments No. Blank/no idea 9
Events for girls 8
Outreach to schools 7
Change perceptions 5
Role models 2
Scholarships/bursaries 2
Modify curriculum 2
Emphasise employment opportunities 1
Table L33: Additional strategies suggested by ACDICT to increase female enrolments
Curriculum Features Appealing to Women No. Social or people-oriented 12
Blank/no idea 9
Communication 4
Holistic/big picture explanations and views 4
Design/creativity 3
Problem-solving 2
Same as men 2
Role models 1
Technology 1
Table L34: Curriculum features identified by members of ACDICT that appeal to women
212
Curriculum Features Appealing to Men No. Technology/Hardware 9
Blank/no idea 8
Games 6
Programming 4
Soft/creative/analytical aspects 3
Detailed view 1
Networking 1
Table L35: Curriculum features identified by ACDICT that appeal to men
Measures to Ensure Curriculum Gender-inclusive No. Blank/no idea/none 22
Group/team work 5
Engaging projects 5
Gender neutrality 6
Table L36: Measures identified by ACDICT to ensure ICT curriculum is gender-inclusive
SD D N A SA 1. ICT is generally male oriented with a masculine
culture 6 4.5%
24 18.2%
27 20.5%
56 42.4%
19 14.4%
2. My workplace is male dominated with a masculine culture
9 6.9%
24 18.3%
37 28.2%
54 41.2%
7 5.4%
3. Female perspectives and approaches to ICT issues and challenges are valuable
0 0%
2 1.5%
12 9.3%
56 42.7%
61 46.6%
4. In my workplace, female perspectives and approaches to ICT issues and challenges are actively sought
3 2.3%
15 11.4%
45 34.4%
41 31.3%
27 20.6%
Table L 37: Frequency of rankings of statements on workplace culture and environment
(SD = strongly disagree, SA = strongly agree)
213
SD D N A SA ICT has a poor perception amongst the general public 2
1.5% 40 30.3%
33 25%
44 33.3%
13 9.9%
The ICT industry in general tries to improve those perceptions
6 4.5%
26 19.7%
33 25%
63 47.7%
4 3%
Industry attempts to improve perceptions are successful
8 6%
32 24.3%
77 58.3%
14 10.6%
1 0.8%
My company makes efforts to improve ICT perceptions amongst the general public
5 3.8%
32 24.5%
40 30.5%
40 30.5%
14 10.7%
These efforts by my company are successful 7 5.4%
10 7.7%
85 65.9%
25 19.4%
2 1.6%
Marketing ICT as a profession will improve the perceptions of the ICT industry amongst the general public
3 2.3%
7 5.5%
26 20.3%
55 43%
37 28.9%
Perceptions of ICT would be improved if there were a clear distinction between ICT occupations or careers in the ICT industry
4 3.1%
11 8.5%
44 33.8%
45 34.6%
26 20%
ICT perceptions would be improved if professionalism in ICT was identified and distributed across existing professions such as engineering, business or the design industry
5 3.9%
15 11.6%
37 28.7%
48 37.2%
24 18.6%
Professional ICT accreditation by a professional body improves the perceptions of ICT
4 3.1%
6 4.7%
40 31.3%
42 32.8%
36 28.1%
Table L38: Frequency of rankings of statements on public perceptions of ICT
(SD = strongly disagree, SA = strongly agree)
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